[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

US20150025405A1 - Acute lung injury (ali)/acute respiratory distress syndrome (ards) assessment and monitoring - Google Patents

Acute lung injury (ali)/acute respiratory distress syndrome (ards) assessment and monitoring Download PDF

Info

Publication number
US20150025405A1
US20150025405A1 US14/379,376 US201314379376A US2015025405A1 US 20150025405 A1 US20150025405 A1 US 20150025405A1 US 201314379376 A US201314379376 A US 201314379376A US 2015025405 A1 US2015025405 A1 US 2015025405A1
Authority
US
United States
Prior art keywords
ali
patient
computing
values
canceled
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Abandoned
Application number
US14/379,376
Inventor
Srinivasan Vairavan
Caitlyn Chiofolo
Nicolas Chbat
Monica Ghosh
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Koninklijke Philips NV
Original Assignee
Koninklijke Philips NV
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Koninklijke Philips NV filed Critical Koninklijke Philips NV
Priority to US14/379,376 priority Critical patent/US20150025405A1/en
Assigned to KONINKLIJKE PHILIPS N.V. reassignment KONINKLIJKE PHILIPS N.V. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: GHOSH, MONISHA, CHBAT, NICOLAS WADIH, CHIOFOLO, Caitlyn Marie, VAIRAVAN, Srinivasan
Publication of US20150025405A1 publication Critical patent/US20150025405A1/en
Abandoned legal-status Critical Current

Links

Images

Classifications

    • G06F19/34
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/08Detecting, measuring or recording devices for evaluating the respiratory organs
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271Specific aspects of physiological measurement analysis
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H15/00ICT specially adapted for medical reports, e.g. generation or transmission thereof
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • the following relates to the medical monitoring arts, clinical decision support system arts, intensive care monitoring and patient assessment arts, and so forth.
  • ALI Acute lung injury
  • ICU intensive care unit
  • ALI is also sometimes known as Acute Respiratory Distress Syndrome (ARDS).
  • ARDS Acute Respiratory Distress Syndrome
  • ALI prediction score One approach for detection or prediction of ALI is known as the ALI prediction score, which uses chronic and acute illness information to identify patients who are more likely to develop ALI during their stay. This approach, however, provides little insight into the timing of development.
  • ALI sniffer Another known approach is the ALI sniffer, which is an electronic system for surveying patients' electronic medical records for evidence of ALI.
  • the ALI sniffer is highly sensitive and specific. However, it applies the current ALI definition to the medical record, which is defined in terms of arterial blood gas (ABG) and chest radiograph characteristics. Thus, the ALI sniffer is limited by its reliance on availability of ABG analysis and chest x-ray tests for the patient.
  • Obtaining and utilizing radiographic evidence of bi-lateral infiltrates signifying ALI can be resource intensive, time consuming, and deleterious to the patient, and in many ICU cases the relevant data is not available at least during the critical initial stages of patient admission and triage.
  • a non-transitory storage medium stores instructions executable by an electronic data processing device including a display to monitor a patient for acute lung injury (ALI) by operations including: (i) receiving values of a plurality of physiological parameters for the patient; (ii) computing an ALI indicator value based at least on the received values of the plurality of physiological parameters for the patient; and (iii) displaying a representation of the computed ALI indicator value on the display.
  • ALI acute lung injury
  • an apparatus comprises an electronic data processing device including a display, and a non-transitory storage medium as set forth in the immediately preceding paragraph operatively connected with the electronic data processing device to execute the instructions stored on the non-transitory storage medium to monitor a patient for acute lung injury (ALI).
  • ALI acute lung injury
  • a method comprises: receiving values of a plurality of physiological parameters for a patient in an intensive care unit (ICU) at an electronic data processing device including a display; using the electronic data processing device, computing an indicator value for a medical condition (which in some embodiments is ALI) based at least on the received values of the plurality of physiological parameters for the patient using an inference algorithm trained on a training set comprising reference patients to distinguish between reference patients having the medical condition and reference patients not having the medical condition; and displaying a representation of the computed indicator value on the display of the electronic data processing device.
  • ICU intensive care unit
  • One advantage resides in providing ALI assessment with timely and available data without solely relying upon radiographic data (e.g. x-rays) or laboratory tests (e.g., arterial blood gas, ABG, analysis).
  • radiographic data e.g. x-rays
  • laboratory tests e.g., arterial blood gas, ABG, analysis.
  • Another advantage resides in providing ALI assessment that takes into account the impact of drugs or medications administered to the patient.
  • Another advantage resides in providing ALI assessment that is readily integrated with existing patient monitors commonly used in intensive care and triage settings.
  • the invention may take form in various components and arrangements of components, and in various process operations and arrangements of process operations.
  • the drawings are only for the purpose of illustrating preferred embodiments and are not to be construed as limiting the invention.
  • FIG. 1 diagrammatically shows a patient in an intensive care unit (ICU) being monitored for acute lung injury (ALI) at a bedside monitor and at a nurses' station, the latter along with other patients in the ICU.
  • ICU intensive care unit
  • ALI acute lung injury
  • FIGS. 2-4 illustrate an ALI detection approach employing Lempel-Ziv complexity metrics computed for monitored vital signs.
  • FIG. 5 illustrates experimental results for a logistic regression-based approach for ALI detection.
  • FIGS. 6-7 illustrate a log-likelihood ratio (LLR)-based approach for ALI detection.
  • LLR log-likelihood ratio
  • FIG. 8 shows a generic aggregation approach for computing an indicator for a medical condition as an aggregation of constituent indicator algorithms.
  • FIGS. 9-15 illustrate application of the aggregation approach of FIG. 8 to a set of constituent ALI indicator algorithms to generate an aggregate ALI indicator.
  • FIGS. 16-19 illustrate displays during various phases of operation of multi-patient monitoring employing an overview display ( FIGS. 16-17 ) and zoom-in displays for a selected patient ( FIGS. 18-19 ).
  • a patient 8 is monitored by a bedside patient monitor 10 , which displays trend data for various physiological parameters of the patient 8 .
  • Terms such as “physiological parameters”, “vital signs”, or “vitals” are used interchangeably herein).
  • illustrative electrocardiograph (ECG) electrodes 12 suitably monitor heart rate and optionally full ECG traces as a function of time.
  • any physiological parameter of medical interest may be monitored, such as by way of illustrative example on or more of the following: heart rate (HR); respiration rate (RR); systolic blood pressure (SBP); diastolic blood pressure (DBP); fraction of inspired oxygen (FiO 2 ); partial pressure of oxygen in arterial blood (PaO 2 ); positive end-expiratory pressure (PEEP); blood hemoglobin (Hgb); and so forth.
  • HR heart rate
  • RR respiration rate
  • SBP systolic blood pressure
  • DBP diastolic blood pressure
  • FiO 2 fraction of inspired oxygen
  • PaO 2 partial pressure of oxygen in arterial blood
  • PEEP positive end-expiratory pressure
  • Hgb blood hemoglobin
  • the patient monitor 10 includes a display 14 , which is preferably a graphical display, on which physiological parameters and optionally other patient data are displayed using numeric representations, graphical representations, trend lines, or so forth.
  • the patient monitor 10 further includes one or more user input devices, such as illustrative controls 16 mounted on the body of the monitor 10 , a set of soft keys 18 shown on the display 14 (which is suitably a touch-sensitive display in such a configuration), a pull-out keyboard, various combinations thereof, or so forth.
  • the user input device(s) enable a nurse or other medical person to configure the monitor 10 (e.g. to select the physiological parameters or other patient data to be monitored and/or displayed), to set alarm settings, or so forth.
  • the patient monitor 10 may include other features such as a speaker for outputting an audio alarm if appropriate, one or more LEDs or lamps of other types to output visual alarms, and so forth.
  • the patient monitor 10 is an “intelligent” monitor in that it includes or is operatively connected with data processing capability provided by a microprocessor, microcontroller, or the like connected with suitable memory and other ancillary electronics (details not illustrated).
  • the patient monitor 10 includes internal data processing capability in the form of a built-in computer, microprocessor, or so forth, such that the patient monitor can perform autonomous processing of monitored patient data.
  • the patient monitor is a “dumb terminal” that is connected with a server or other computer or data processing device that performs the processing of patient data. It is also contemplated for a portion of the data processing capability to be distributed amongst intercommunicating body-worn sensors or devices mounted on the patient 8 , e.g. in the form of a Medical Body Area Network (MBAN).
  • MBAN Medical Body Area Network
  • the patient 8 is disposed in a patient room of an intensive care unit (ICU), which may for example be a medical ICU (MICU), a surgical ICU (SICU), a cardiac care unit (CCU), a triage ICU (TRICU), or so forth.
  • ICU intensive care unit
  • the patient is typically monitored by the bedside patient monitor 10 located with the patient (e.g., in the patient's hospital room) and also by an electronic monitoring device 20 with suitable display 22 (e.g. a dedicated monitor device or a suitably configured computer) located at a nurses' station 24 .
  • the ICU has one or more such nurses' stations, with each nurses' station assigned to a specific set of patients (which may be as few as a single patient in extreme situations).
  • a wired or wireless communication link conveys patient data acquired by the bedside patient monitor 10 to the electronic monitoring device 20 at the nurses' station 24 .
  • the communication link 26 may, for example, comprise a wired or wireless Ethernet (dedicated or part of a hospital network), a Bluetooth connection, or so forth. It is contemplated for the communication link 26 to be a two-way link i.e., data also may be transferrable from the nurses' station 24 to the bedside monitor 10 .
  • the bedside patient monitor 10 is configured to detect and indicate Acute Lung Injury (ALI) by performing data processing as disclosed herein on information including at least one or more physiological parameters monitored by the patient monitor 10 .
  • the electronic monitoring device 20 at the nurses' station 24 may be configured to detect and indicate ALI by performing data processing as disclosed herein on information including at least one or more physiological parameters monitored by the patient monitor 10 .
  • ALI Acute Lung Injury
  • ARDS Acute Respiratory Distress Syndrome
  • the ALI detection as disclosed herein is based on physiological parameters such as HR, RR, SBP, DBP, FiO 2 , PEEP, or so forth, which are monitored by the patient monitor 10 and hence are available in real-time. Patient data with longer acquisition latency times, such as radiography reports and laboratory findings (e.g. PaO 2 , Hgb, et cetera) are not utilized or are utilized as supplemental information for evaluating whether ALI is indicated.
  • FIGS. 2-4 an embodiment employing Lempel-Ziv complexity-based detection of ALI is described.
  • the patient 8 is admitted to the ICU (indicated by block 30 ).
  • drugs/medications drugs/medications
  • the drug administration data stream 36 can take various forms, such as a binary data stream (e.g. value “0” as a function of (optionally discretized) time except during a drug administration event which is indicated by a value “1”.
  • a binary data stream e.g. value “0” as a function of (optionally discretized) time except during a drug administration event which is indicated by a value “1”.
  • the value may be “0” when no drip is being administered and “1” (or some other value) during the administration of the drip.
  • Other value-time representations are also contemplated, e.g. a time-varying value modeling the expected dynamic drug concentration in the patient (or in an organ of interest) from initial administration until the drug is removed from the body by the kidneys or other mechanism.
  • the Lempel-Ziv complexity metric (see e.g. A. Lempel and J. Ziv, “On the complexity of finite sequences,” IEEE Trans. Inform. Theory, vol. IT-22, pp. 75-81, 1976) is computed for each of the vital sign data streams 34 and for the drug administration data stream 36 .
  • This generates a Lempel-Ziv complexity metric 44 corresponding to each vital sign data stream 34 , and a Lempel-Ziv complexity metric 46 corresponding to the drug administration data stream 36 .
  • the Lempel-Ziv complexity metrics 44 , 46 are combined by an addition 50 (optionally with weighting of the data streams) or by another aggregation operator to generate an additive complexity value that is then thresholded by a thresholder 52 to generate a binary ALI indicator 54 having a positive (or other designated) value indicating the patient exhibits ALI or a negative (or other designated) value indicating the patient does not exhibit ALI.
  • Lempel-Ziv complexity is used to quantify the complexity of different time series signals such as electroencephalography (EEG), heart rate, blood pressure, and so forth.
  • EEG electroencephalography
  • the input is a vital sign data stream 34 or the drug administration data stream 36 .
  • Lempel-Ziv (LZ) complexity is based on coarse-graining the data stream, i.e. discretizing the data stream in the time (if not already acquired as discrete samples) and value dimensions. In illustrative FIG.
  • the data stream is assumed to already be acquired as discrete time samples, and the value is coarse-grained by converting the numerical data into binary values, e.g “0” if the value is below a threshold T d or “1” if the value is above the threshold T d .
  • Other coarsening approaches are contemplated, e.g. discretizing to a more granular sequence (0, 1, 2, . . . , N) using multiple thresholds.
  • the output of this operation is the coarse-grained, e.g binary, data stream 60 .
  • the LZ complexity is a measure of the amount of distinct patterns available in the sequence, or more particularly within a time interval or time window n of the sequence.
  • the binary sequence 60 is scanned from left to right over the window n and a complexity counter is incremented by one unit every time a new (sub-)sequence of consecutive characters is encountered.
  • a complexity counter is incremented by one unit every time a new (sub-)sequence of consecutive characters is encountered.
  • some normalization may be applied, e.g.
  • the Lempel-Ziv complexity measure c(n) is expressed in units of new pattern occurrences per unit time. It will be appreciated that the processing shown diagrammatically in FIG. 3 may be repeated for successive (and optionally partially overlapping) time windows n to provide the Lempel-Ziv complexity measure c(n) as a function of (discretized) time.
  • the adder 50 is suitably c HR (n)+c SBP (n)+c DBP (n)+c RR (n)+c Drugs (n).
  • the output may be written as w HR c HR (n)+w SBP c SBP (n)+w DBP c DBP (n)+w RR c RR (n)+w Drugs c Drugs (n) where the w terms are scalar weights.
  • ROC Receiver Operating Characteristics
  • LZ Lempel-Ziv
  • FIG. 4 shows the results for the training population, where the area under the ROC curve is 0.73 and the optimal threshold is 5.92 (sensitivity: 63% and specificity: 75%).
  • the optimal threshold is marked by a black square in FIG. 4 .
  • the logistic regression model involves a nonlinear mapping of the independent or predictor variables such as heart rate (HR), respiratory rate (RR), non-invasive blood pressure measurement (NIBP-m), or so forth, to the dependent or response variable (e.g. ALI or control in the illustrative examples) through the logistic regression function or logit transformation.
  • HR heart rate
  • RR respiratory rate
  • NIBP-m non-invasive blood pressure measurement
  • a suitable formulation is
  • p denotes the probability of ALI
  • ⁇ 0 is a constant
  • ⁇ 1 . . . ⁇ i are coefficients of the predictors x 1 . . . x i (e.g., the HR, RR, NIBP-m, et cetera).
  • ⁇ 0 is again a constant
  • ⁇ right arrow over ( ⁇ ) ⁇ is a vector of the coefficients of the predictors
  • p is again probability of ALI
  • y is the true presence/absence of ALI.
  • the coefficients are computed using minimization techniques such as the ordinary least squares (OLS) or the maximum likelihood estimator (MLE).
  • the logistic regression model used three features as input: HR, RR, and HR/NIBP-m, to yield a probability of ALI development.
  • HR constant ⁇ 0 and coefficients ⁇ right arrow over ( ⁇ ) ⁇ were derived from a 600 patient dataset comprising 300 controls and 300 ALI patients using the foregoing equations.
  • the model was applied continuously (in other words, applied to each unique time point for a patient) and a receiver operator characteristic (ROC) curve was drawn to determine the threshold providing the desired level of sensitivity and specificity.
  • ROC receiver operator characteristic
  • the model was then applied in the same continuous manner to a validation set of unseen patient data comprising 6,690 controls and 326 ALI patients. An ROC curve was again drawn and the sensitivity and specificity at the previously determined threshold were compared to those obtained from the derivation dataset.
  • FIG. 5 shows the results. Performance of the logistic regression model on the training data resulted in 71.00% sensitivity and 74.33% specificity. Using the same threshold, performance of the model on the validation data resulted in 63.19% sensitivity and 81.05% specificity.
  • EMRs electronic medical records
  • LLR log-likelihood ratio
  • N the total number of patients in a derivation (i.e. training) data set, of which N 1 have the disease (ALI in the illustrative example) and N 0 do not have the disease.
  • D the total number of patients in a derivation (i.e. training) data set, of which N 1 have the disease (ALI in the illustrative example) and N 0 do not have the disease.
  • d [d 1 d 2 . . .
  • the joint log-likelihood ratio of all the parameters is the sum of the log-likelihood of the individual parameters.
  • FIG. 6 shows the testing phase.
  • the log-likelihood ratio LLR( d ) is computed in an operation 74 for a patient with input patient data vector d whose elements [d 1 d 2 . . . d L ] store patient data for the patient under test.
  • the ALI detection then proceeds using a threshold operation 76 as follows:
  • T is an optimum detection threshold determined from the training data set.
  • results for an actually performed log-likelihood ratio-based ALI test are reported.
  • An ROC analysis is used in order to obtain the optimal threshold T for the threshold operation 76 .
  • ROC analysis for LLR was performed on 506 ICU patients (training dataset), of which 206 where ALI and 300 were controls. The results of the training population are shown in FIG. 7 .
  • the area under the ROC curve is 0.88 and the optimal threshold is 2.6 (sensitivity: 86% and specificity: 77%). As more data sets are obtained for training the thresholds and performance values may change.
  • the optimal threshold is marked as a black square in the plot.
  • the threshold obtained from the training data is also shown in FIG. 7 in its corresponding location on the ROC curve generated from testing data.
  • the approach achieved a specificity (84%) and sensitivity (72%) in the testing datasets.
  • Location of the operating point (training threshold T) changed slightly in the testing datasets, with decreased sensitivity and increased specificity.
  • the threshold is fairly robust considering the increased specificity.
  • the approach also has an area under the ROC curve (0.86) for testing datasets very close to that of the training datasets (0.87) which is advantageous for reliable ALI detection.
  • LZ Lempel-Ziv complexity metric
  • LR logistic regression-based approach
  • LLR log-likelihood ratio-based approach
  • LLR log-likelihood ratio-based approach
  • the outputs of set of N algorithms 80 are aggregated at an aggregation block 82 to generate an organ status indicator 84 that is suitably displayed and/or trended as a function of time on the bedside monitor 10 , nurses' station monitoring device 20 , (see FIG. 1 ) or so forth.
  • the generic framework of FIG. 8 is not disease-specific.
  • a first algorithm is based on a distillation of physicians' expertise.
  • this is implemented as a fuzzy inference algorithm 90 that is built from linguistic (or fuzzy) information about relationships of variables and run using a set of decision rules 92 constructed based on clinical information 94 collected in discussions with physicians.
  • the fuzzy inference algorithm 90 may, for example, constitute a clinical decision support system (CDSS) component.
  • CDSS clinical decision support system
  • a second algorithm is based on distillation of relevant clinical literature.
  • this is implemented as a Bayesian network 100 that is structured from probabilities 102 computed based on clinical research 104 .
  • a clinical study may indicate that statistically a combination of parameters is indicative of ALI with a probability P.
  • a third algorithm is based on the translation of pathophysiology in terms of causal relationships between variables (such as RR, HR, etc.).
  • Potential causes of ALI development could be mechanical, chemical, or biological in nature.
  • mechanical causes of ALI include fast/deep breathing and/or ventilation settings. Examples of mechanical conditions are:
  • this is implemented as a state machine 110 implementing a logic flow 112 quantifying a clinical definition 114 .
  • the state machine 110 outputs ALI negative, while if any of the three conditions is met then the state machine 110 outputs ALI positive.
  • the fourth, fifth, and sixth algorithms are data-based, and in illustrative FIG. 9 correspond to the LLR algorithm 120 , LZ algorithm 130 , and LR algorithm 140 , respectively, described herein with reference to FIGS. 2-7 .
  • These algorithms 120 , 130 , 140 are based on ICU data 142 such as vitals, labs, and interventions (e.g. drug administration events), and are optionally also based on pre-ICU data 144 such as demographic data and/or known chronic diseases or conditions of the patient.
  • pre-ICU indicates that such patient information are typically gathered prior to the patient being admitted to the ICU as part of the admissions procedures; however, the pre-ICU data 144 may in some cases be generated, in whole or in part, after the patient enters the ICU).
  • the aggregation block 82 may be implemented in various ways. In the illustrative ALI application of FIG. 9 , the aggregation block 82 is implemented by linear discriminant analysis (LDA) or by a voting system (SOFALI). These illustrative aggregation approaches are described in turn in the following.
  • LDA linear discriminant analysis
  • SOFALI voting system
  • the linear discriminant function for each class k can be represented as:
  • y k ⁇ ( x ) - ( 1 2 ) ⁇ ⁇ k ⁇ C - 1 ⁇ ⁇ k T + log ⁇ ( p k ) + ( ⁇ k T ⁇ C - 1 ) ⁇ x
  • LDA coefficients are obtained for the different predictor variables (i.e., different algorithms) on the training data set. LDA coefficients are then suitably passed through a softmax transformation in order to convert the coefficients to probabilities p k according to:
  • the voting system aggregator is suitably implemented as follows.
  • the thresholds of the knowledge-based and data-based approaches are obtained from the training data set. These individual thresholds are then used to obtain a voting system based ALI detection (based on the number of algorithms detecting ALI).
  • TABLE 1 shows the illustrative voting system (SOFALI) employed for integrating the six different algorithms of illustrative FIG. 9 .
  • FIGS. 12 and 13 trajectories of the integrative LDA approach are shown for an illustrative ALI patient ( FIG. 12 ) and for a control patient ( FIG. 13 ).
  • FIGS. 14 and 15 trajectories of the integrative SOFALI approach are shown for an illustrative ALI patient ( FIG. 14 ) and for a control patient ( FIG. 15 ).
  • FIGS. 12-15 demonstrate that both the LDA and SOFALI integrative approaches detected ALI early as compared to the retrospectively determined ALI onset time by the physician.
  • the aggregation embodiment described with reference to FIG. 9 is merely illustrative, and numerous variants are contemplated.
  • the set of algorithms can be different from the illustrative six algorithms of FIG. 9 .
  • Aggregation algorithms other than LDA or SOFALI are also contemplated, such as aggregation based on a distance metric or based on decision trees or so forth.
  • ALI/ARDS Acute Kidney Injury (AKI), Disseminated Intravascular coagulation (DIC), using suitable vital signs and optionally other features such as the illustrative drug administration data stream, and training on suitable training data sets to optimize the inference algorithm parameters.
  • AMI Acute Kidney Injury
  • DIC Disseminated Intravascular coagulation
  • the ALI status indicator computed by any of the disclosed algorithms may be utilized in various ways.
  • the ALI status indicator may be displayed and optionally logged on the bedside monitor 10 and/or displayed and optionally logged at the nurses' station electronic monitoring device 20 (see FIG. 1 ).
  • the display can be numeric, and/or in the form of a trend line plotting ALI status indicator value versus time.
  • an inference engine that generates a value that is thresholded to generate an ALI positive (or negative) indication, it is contemplated to additionally or alternatively display the value without thresholding.
  • the ALI value generated by the inference engine may be plotted as a trend line with the ALI positive/negative threshold shown as a horizontal line superimposed on the trend line graph.
  • multiple thresholds may be applied to correspond to increasing disease severity or increasing probability of ARDS.
  • Color coding can be applied to indicate the level of severity of the threshold.
  • the ALI status indicator can serve as input to a clinical decision support system (CDSS), serving as one piece of data used in conjunction with other data in generating clinical recommendations for consideration by the physician.
  • CDSS clinical decision support system
  • the ALI status indicator is typically not accepted as a diagnosis, but rather the ALI status indicator serves as one piece of data for consideration by the patient's physician or other expert medical personnel in deciding the most appropriate course of treatment for the patient.
  • a typical ICU services several patients at any given time. Each of these patients may (at least in general) be susceptible to ALI/ARDS, and is advantageously monitored for this condition using techniques disclosed herein.
  • the ICU is a stressful and complex environment, and additional information such as a set of ALI status indicators for the patients in the ICU may contribute to information overload.
  • This multi-patient monitoring display is suitably employed at the nurses' station electronic monitoring device 20 (see FIG. 1 ) to provide monitoring of all patients under the care of the nurse or nurses (or other medical personnel) assigned to the nurses' station.
  • an illustrative overview multi-patient monitoring display 200 is suitably shown on the nurses' station electronic monitoring device 20 of FIG. 1 .
  • the illustrative overview display 200 diagrammatically represents each patient in the current ICU (the medical ICU, i.e. MICU, in illustrative FIG. 16 ) by a box containing the most pertinent information, in the illustrative example including the patient identification (PID) number and the ALI status indicator value for the patient, represented in illustrative FIG. 16 by the SOFALI aggregation value (more generally, any of the ALI status indicators disclosed herein, with or without aggregation, may be employed).
  • PID patient identification
  • SOFALI aggregation value more generally, any of the ALI status indicators disclosed herein, with or without aggregation, may be employed.
  • the boxes diagrammatically representing the patients are laid out on the display 200 in a manner mimicking the physical layout of the patients in the ICU.
  • the illustrative MICU has ten beds laid out in a “C” pattern and all ten beds are occupied by patients. If a bed was unoccupied, this could be suitably represented by employing an empty box for that bed or by omitting the representative box entirely.
  • each of the diagrammatic boxes is optionally color-coded to represent the ALI status of the patient.
  • the color coding is diagrammatically represented by different cross-hatchings, with patients having SOFALI index values 0 or 1 being one color (e.g. green or white or no color), patients having SOFALI index values 2 or 3 being a different color (e.g. yellow to indicate a “watch” status for these patients), and patients having SOFALI of 4 (or possibly greater) being yet a different color (e.g. red to indicate a serious ALI or ARDS condition).
  • the color-coding can correspond to severity of illness and a change in color can correspond to a new threshold or boundary of a score ranging.
  • a score ranging from 0 to 100 0 to 50 can represent a low risk group
  • 50 to 75 can indicate a medium risk (“watch” or “warning”) group
  • above 75 can indicate a high risk group.
  • the overview display 200 optionally includes a drop-down menu 202 or other graphical user interface (GUI) dialog enabling a nurse or other operator to switch to a different ICU unit.
  • GUI graphical user interface
  • the information contained in the diagrammatic boxes of the overview display 200 is merely an illustrative example, and additional or other information may be shown.
  • patients may be identified by name instead of or in addition to by PID number.
  • Other serious conditions may be indicated instead of or in addition to ALI. If two or more conditions are indicated and are to be represented by color coding, the color coding may be shown in different areas of the box, or the entire box may be color coded by the color representing the most serious condition (e.g. “red” if any represented condition has a “red” status color, even if some other displayed condition would be “yellow” or “white”).
  • the multi-patient overview display provides a quick “snapshot” overview of critical health status of a group of patients in the ICU, or in other locales (e.g. ED, OR, ward, etc.), via diagrammatic health status blocks.
  • one or more of the following may be incorporated: (1) individual color-coded block with numeric value and label (e.g. overall health); (2) individual color-coded block with numeric value and label (e.g. ALI health); (3) Multiple color-coded blocks contained within a single block with numeric values and labels (e.g. acute lung injury, acute kidney injury, disseminated intravascular coagulation, acute myocardial infarction, et cetera); or so forth.
  • each diagrammatic block of the overview display provides an overall view of critical illness status of an individual patient, and the collection of blocks in the overview display thus provides this information for all patients in the ICU.
  • zoomed-in view of the status of the selected patient is shown in a zoomed-in patient display 210 ( FIG. 18 ) or alternative-embodiment zoomed-in patient display 220 ( FIG. 19 ).
  • the zoomed-in display shows a view of ALI/ARDS development (and/or development of another monitored condition), in time, for an individual patient.
  • the zoomed-in display may show predicted development in a given number of hours in the future.
  • An ALI status indicator may be displayed as a value (optionally quantized) and corresponding color for all organ health assessment scores used in the ICUs (e.g. SOFA, AKIN criteria, et cetera, other contemplated scores including by way of illustrative example quantized CDS indicators for ALI, AKI, et cetera) in one concise, easy to read “snapshot” display.
  • Trend indicators may be shown in various formats, such as using+/ ⁇ signs, or up, down, horizontal arrows, by various color coding schemes (solid: traffic light pattern; spectrum-like: heatmap pattern; or so forth), by positive/negative numerical values, increased/decreased position on a vertical axis, or so forth.
  • the combination of the overview display and the patient-specific zoom-in display provides a quick and easy mechanism for changing views/interfaces for groups of patients or individual patients and enables focusing on ALI or another organ system or syndrome of interest.
  • CDSS capability is incorporated to aid in decision making via display of suggested/recommended algorithm decision thresholds and in other embodiments, confidence intervals or bounds on this decision threshold.
  • the zoomed-in view optionally shows results of constituent algorithms of the aggregation, optionally trended in time, that contribute to the aggregated algorithm output. While rectangular diagrammatic boxes are illustrated, markers used for organ health status can be of other shapes and of various sizes (e.g. actual traffic light, speedometer, or organ shape/image that changes color).
  • organ health information may be visualized via functionality including (by way of illustrative example): plotting; re-plotting from different starting points; animated plotting; pausing/resuming simulations; zooming (e.g. one-hour trends instead of six-hour trends); and so forth.
  • age of information, new or (carried) zero order held values can be depicted via mechanisms such as filled/unfilled markers, outlined/not outlined markers, bolded/not bolded marker outlines, and so forth.
  • FIGS. 16-19 are described in further detail in the following.
  • a group overview display 200 is shown for an MICU including ten beds all occupied by patients. If a bed is empty, the text might say “Bed Empty”, the color might be light gray or faded, the action functions of the block are disabled, etc. If a bed is occupied, the block is labeled with a patient identifier (e.g. PID 123456).
  • the text also includes a label and numeric value for the score of the organ indicator (e.g. ALI indicator SOFALI indicating severity of the ALI). Green, yellow, and red indicate low, medium, and high risk of ALI, respectively.
  • the color can be a spectrum of colors from lighter to darker hues.
  • the color and score may indicate an overall organ health (e.g. respiratory, cardiovascular, renal, etc.).
  • the scores for other organs can also be depicted.
  • the block is optionally segmented or has several components for each organ system, where each has the respective color and score indicating that organ's health.
  • the overview display 200 of FIG. 16 is interacted with by a nurse to select another ICU (e.g. medical, surgical, trauma, etc.) via the drop-down GUI dialog 202 .
  • additional groups of patients might include Worst10 (e.g. display the 10 most critically ill patients in all ICU's of the hospital or other medical center).
  • Worst10 e.g. display the 10 most critically ill patients in all ICU's of the hospital or other medical center.
  • User groups and number of beds are as appropriate for the given ICU, and may be configurable for example using a “drag-and-drop” user interface by which a user drags a new bed into the ICU display and links it with a set of input data streams for that bed. (Similarly a bed can be removed by dragging it off the display).
  • the color coding conveys different information, namely being used to identify changes in parameters. For example, if a patient's organ status is declining, this can be reflected by “red” color coding even if the actual level of the ALI or other organ status indicator is not indicating ALI positive in this embodiment the color coding highlights changes rather than absolute values of organ status indicators.
  • a zoomed-in display 210 is shown, which is suitably generated by the nurse selecting (e.g. clicking or double-clicking with a mouse, or touching in the case of a touchscreen) one diagrammatic box of the overview display 200 of FIG. 16 to select an individual patient to which to zoom.
  • the illustrative patient of FIG. 18 has a high risk of ALI.
  • Demographics are displayed in the upper right of the display 210 . Demographics include but are not limited to height, weight, age, gender, predicted body weight, body mass index (BMI), hospital or ICU admission or discharge dates and times, chronic conditions, reasons for admissions, current diagnoses, and so forth.
  • the upper left plot of the display 210 shows current and predicted ALI CDS algorithm output (aggregate SOFALI score on vertical axis, time on horizontal axis).
  • the six lower left plots of the display 210 respectively plot each of the six individual algorithms that are aggregated to obtain the SOFALI score (cf. FIG. 9 ).
  • the recommended decision threshold (and optionally its confidence bounds) are optionally displayed as a line of value y on the vertical axis that spans the horizontal axis.
  • the nurse or other user can select to review a new patient by using the drop down GUI dialog box in the uppermost left.
  • the lower right side of the display shows a matrix of organ system health (SOFALI, cardiovascular, respiratory, renal, hepatic, coagulation) via colored markers over time (different colors are diagrammatically indicated in FIG. 18 by different shading levels). Markers could be different sizes, shapes, or images, can have bolded/non-bolded outlines to distinguish new values from old or carried values, and/or can increase or decrease in position on the vertical axis to represent increases and decreases in scores. Other embodiments could incorporate other clinical assessments (SOFA, AKIN, SIRS, etc.) or newly developed CDS assessments (CDS for ALI, AKI, DIC, etc.) or a combination of both.
  • Selection of scores to be used or displayed is optionally customizable in a selectable preferences, configuration, or set-up window (not shown).
  • the focus organ system or the left side of the display can be changed to other organ systems by selecting a new organ to display.
  • a group or patient group (similar to or some version of figures above) may be displayed in the place of the individual algorithms.
  • the nurse or other use can press a play button to animate plots and review patient health trends and trajectories over time from the start time or a selected time to the current time.
  • Optional pause/resume functionality allows further analysis of particular points of concern. User interfacing for such controls is suitably implemented by user-controllable time slider bars or the like.
  • FIG. 19 an alternative embodiment zoomed-in display 220 is shown, in which the matrix of organ system health in the lower right side of the display is modified to employ a grid with numeric values in the grid cells.
  • the organ system overview on the right side of the GUI includes the color-coding system as previously described (traffic light or spectrum-like, again diagrammatically represented in FIG. 19 by different shading levels).
  • the color represents the current score, though other embodiments may include a numeric value for the current score as well.
  • the “+/ ⁇ ” signs indicate a positive or negative trend from the previous value, where the higher or more positive the SOFA and SOFALI value, the worse the organ health.
  • the numeric value immediately following a “+/ ⁇ ” sign is the delta or change from the previous value. Future embodiments can incorporate combinations of these current values and delta values or can use directional arrows instead of “+/ ⁇ ” signs.
  • the disclosed techniques for detecting ALI or other conditions of concern for ICU patients are suitably implemented by the built-in computer, microprocessor, or so forth of the illustrative bedside monitor 10 and/or of the illustrative nurses' station electronic monitoring device 20 .
  • the disclosed techniques can be embodied by a non-transitory storage medium storing instructions executable by such an electronic data processing device to perform the disclosed detection methods.
  • the non-transitory storage medium may, for example, comprise a hard disk or other magnetic storage medium, random access memory (RAM), read-only memory (ROM), or another electronic storage medium, an optical disk or other optical storage medium, a combination of the foregoing, or so forth.

Landscapes

  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Medical Informatics (AREA)
  • Public Health (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Pathology (AREA)
  • Epidemiology (AREA)
  • Primary Health Care (AREA)
  • Surgery (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Physiology (AREA)
  • Veterinary Medicine (AREA)
  • Animal Behavior & Ethology (AREA)
  • Physics & Mathematics (AREA)
  • Molecular Biology (AREA)
  • Biophysics (AREA)
  • Databases & Information Systems (AREA)
  • General Business, Economics & Management (AREA)
  • Business, Economics & Management (AREA)
  • Data Mining & Analysis (AREA)
  • Artificial Intelligence (AREA)
  • Signal Processing (AREA)
  • Psychiatry (AREA)
  • Pulmonology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)
  • Medical Treatment And Welfare Office Work (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

A patient is monitored for a medical condition such as acute lung injury (AL1) by operations including: (i) receiving values of a plurality of physiological parameters for the patient; (ii) computing an AL1 indicator value based at least on the received values of the plurality of physiological parameters for the patient; and (iii) displaying a representation of the computed AL1 indicator value on a display (14, 22). The computing operation (ii) may employ various inference algorithms trained on a training set comprising reference patients to distinguish between reference patients having AL1 and reference patients not having AL1, or may employ an aggregation of two or more such inference algorithms. If patients in an ICU are monitored, the display (22) may simultaneously display a diagrammatic representation of each patient including an identification of the patient and a representation of the AL1 indicator value for the patient.

Description

  • The following relates to the medical monitoring arts, clinical decision support system arts, intensive care monitoring and patient assessment arts, and so forth.
  • Acute lung injury (ALI) is a devastating complication of acute illness and one of the leading causes of multiple organ failure and mortality in the intensive care unit (ICU). ALI is also sometimes known as Acute Respiratory Distress Syndrome (ARDS). ALI is estimated to be prevalent in 7-10% of all ICU patients, and exhibits a high mortality of greater than 40% after hospital discharge. However, less than one-third of ALI patients are detected by ICU physicians.
  • One approach for detection or prediction of ALI is known as the ALI prediction score, which uses chronic and acute illness information to identify patients who are more likely to develop ALI during their stay. This approach, however, provides little insight into the timing of development. Another known approach is the ALI sniffer, which is an electronic system for surveying patients' electronic medical records for evidence of ALI. The ALI sniffer is highly sensitive and specific. However, it applies the current ALI definition to the medical record, which is defined in terms of arterial blood gas (ABG) and chest radiograph characteristics. Thus, the ALI sniffer is limited by its reliance on availability of ABG analysis and chest x-ray tests for the patient. Obtaining and utilizing radiographic evidence of bi-lateral infiltrates signifying ALI can be resource intensive, time consuming, and deleterious to the patient, and in many ICU cases the relevant data is not available at least during the critical initial stages of patient admission and triage.
  • The following contemplates improved apparatuses and methods that overcome the aforementioned limitations and others.
  • According to one aspect, a non-transitory storage medium stores instructions executable by an electronic data processing device including a display to monitor a patient for acute lung injury (ALI) by operations including: (i) receiving values of a plurality of physiological parameters for the patient; (ii) computing an ALI indicator value based at least on the received values of the plurality of physiological parameters for the patient; and (iii) displaying a representation of the computed ALI indicator value on the display.
  • According to another aspect, an apparatus comprises an electronic data processing device including a display, and a non-transitory storage medium as set forth in the immediately preceding paragraph operatively connected with the electronic data processing device to execute the instructions stored on the non-transitory storage medium to monitor a patient for acute lung injury (ALI).
  • According to another aspect, a method comprises: receiving values of a plurality of physiological parameters for a patient in an intensive care unit (ICU) at an electronic data processing device including a display; using the electronic data processing device, computing an indicator value for a medical condition (which in some embodiments is ALI) based at least on the received values of the plurality of physiological parameters for the patient using an inference algorithm trained on a training set comprising reference patients to distinguish between reference patients having the medical condition and reference patients not having the medical condition; and displaying a representation of the computed indicator value on the display of the electronic data processing device.
  • One advantage resides in providing ALI assessment with timely and available data without solely relying upon radiographic data (e.g. x-rays) or laboratory tests (e.g., arterial blood gas, ABG, analysis).
  • Another advantage resides in providing ALI assessment that takes into account the impact of drugs or medications administered to the patient.
  • Another advantage resides in providing ALI assessment that is readily integrated with existing patient monitors commonly used in intensive care and triage settings.
  • Numerous additional advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description.
  • The invention may take form in various components and arrangements of components, and in various process operations and arrangements of process operations. The drawings are only for the purpose of illustrating preferred embodiments and are not to be construed as limiting the invention.
  • FIG. 1 diagrammatically shows a patient in an intensive care unit (ICU) being monitored for acute lung injury (ALI) at a bedside monitor and at a nurses' station, the latter along with other patients in the ICU.
  • FIGS. 2-4 illustrate an ALI detection approach employing Lempel-Ziv complexity metrics computed for monitored vital signs.
  • FIG. 5 illustrates experimental results for a logistic regression-based approach for ALI detection.
  • FIGS. 6-7 illustrate a log-likelihood ratio (LLR)-based approach for ALI detection.
  • FIG. 8 shows a generic aggregation approach for computing an indicator for a medical condition as an aggregation of constituent indicator algorithms.
  • FIGS. 9-15 illustrate application of the aggregation approach of FIG. 8 to a set of constituent ALI indicator algorithms to generate an aggregate ALI indicator.
  • FIGS. 16-19 illustrate displays during various phases of operation of multi-patient monitoring employing an overview display (FIGS. 16-17) and zoom-in displays for a selected patient (FIGS. 18-19).
  • With reference to FIG. 1, a patient 8 is monitored by a bedside patient monitor 10, which displays trend data for various physiological parameters of the patient 8. (Terms such as “physiological parameters”, “vital signs”, or “vitals” are used interchangeably herein). For example, illustrative electrocardiograph (ECG) electrodes 12 suitably monitor heart rate and optionally full ECG traces as a function of time. Substantially any physiological parameter of medical interest may be monitored, such as by way of illustrative example on or more of the following: heart rate (HR); respiration rate (RR); systolic blood pressure (SBP); diastolic blood pressure (DBP); fraction of inspired oxygen (FiO2); partial pressure of oxygen in arterial blood (PaO2); positive end-expiratory pressure (PEEP); blood hemoglobin (Hgb); and so forth.
  • The patient monitor 10 includes a display 14, which is preferably a graphical display, on which physiological parameters and optionally other patient data are displayed using numeric representations, graphical representations, trend lines, or so forth. The patient monitor 10 further includes one or more user input devices, such as illustrative controls 16 mounted on the body of the monitor 10, a set of soft keys 18 shown on the display 14 (which is suitably a touch-sensitive display in such a configuration), a pull-out keyboard, various combinations thereof, or so forth. The user input device(s) enable a nurse or other medical person to configure the monitor 10 (e.g. to select the physiological parameters or other patient data to be monitored and/or displayed), to set alarm settings, or so forth. Although not explicitly shown, the patient monitor 10 may include other features such as a speaker for outputting an audio alarm if appropriate, one or more LEDs or lamps of other types to output visual alarms, and so forth.
  • The patient monitor 10 is an “intelligent” monitor in that it includes or is operatively connected with data processing capability provided by a microprocessor, microcontroller, or the like connected with suitable memory and other ancillary electronics (details not illustrated). In some embodiments the patient monitor 10 includes internal data processing capability in the form of a built-in computer, microprocessor, or so forth, such that the patient monitor can perform autonomous processing of monitored patient data. In other embodiments the patient monitor is a “dumb terminal” that is connected with a server or other computer or data processing device that performs the processing of patient data. It is also contemplated for a portion of the data processing capability to be distributed amongst intercommunicating body-worn sensors or devices mounted on the patient 8, e.g. in the form of a Medical Body Area Network (MBAN).
  • In illustrative examples, the patient 8 is disposed in a patient room of an intensive care unit (ICU), which may for example be a medical ICU (MICU), a surgical ICU (SICU), a cardiac care unit (CCU), a triage ICU (TRICU), or so forth. In such settings, the patient is typically monitored by the bedside patient monitor 10 located with the patient (e.g., in the patient's hospital room) and also by an electronic monitoring device 20 with suitable display 22 (e.g. a dedicated monitor device or a suitably configured computer) located at a nurses' station 24. Typically, the ICU has one or more such nurses' stations, with each nurses' station assigned to a specific set of patients (which may be as few as a single patient in extreme situations). A wired or wireless communication link (indicated diagrammatically by double-arrow-headed curved line 26) conveys patient data acquired by the bedside patient monitor 10 to the electronic monitoring device 20 at the nurses' station 24. The communication link 26 may, for example, comprise a wired or wireless Ethernet (dedicated or part of a hospital network), a Bluetooth connection, or so forth. It is contemplated for the communication link 26 to be a two-way link i.e., data also may be transferrable from the nurses' station 24 to the bedside monitor 10.
  • The bedside patient monitor 10 is configured to detect and indicate Acute Lung Injury (ALI) by performing data processing as disclosed herein on information including at least one or more physiological parameters monitored by the patient monitor 10. Additionally or alternatively, the electronic monitoring device 20 at the nurses' station 24 may be configured to detect and indicate ALI by performing data processing as disclosed herein on information including at least one or more physiological parameters monitored by the patient monitor 10. Note that the terms ALI and Acute Respiratory Distress Syndrome (ARDS) are used interchangeably herein. Advantageously, the ALI detection as disclosed herein is based on physiological parameters such as HR, RR, SBP, DBP, FiO2, PEEP, or so forth, which are monitored by the patient monitor 10 and hence are available in real-time. Patient data with longer acquisition latency times, such as radiography reports and laboratory findings (e.g. PaO2, Hgb, et cetera) are not utilized or are utilized as supplemental information for evaluating whether ALI is indicated.
  • In the following, various embodiments of ALI/ARDS detection are set forth.
  • With reference to FIGS. 2-4, an embodiment employing Lempel-Ziv complexity-based detection of ALI is described. Referencing diagrammatic FIG. 2, the patient 8 is admitted to the ICU (indicated by block 30). There may be scenarios where different drugs/medications (“drugs” and “medications” are used interchangeably herein) may be administered to the patient 8 in order to stabilize the patient (indicated by block 32). The illustrative ALI detection approach of FIG. 2 utilizes illustrative vital signs data streams 34 including heart rate (HR), arterial systolic and diastolic blood pressure (SBP and DBP), and respiratory rate (RR), along with an additional patient data stream 36 comprising instances of the administration 32 of one or more different drugs to the patient 8. The drug administration data stream 36 can take various forms, such as a binary data stream (e.g. value “0” as a function of (optionally discretized) time except during a drug administration event which is indicated by a value “1”. In the case of a drug administered over a time interval, e.g. an intravenous drip, the value may be “0” when no drip is being administered and “1” (or some other value) during the administration of the drip. Other value-time representations are also contemplated, e.g. a time-varying value modeling the expected dynamic drug concentration in the patient (or in an organ of interest) from initial administration until the drug is removed from the body by the kidneys or other mechanism.
  • In a block 40, the Lempel-Ziv complexity metric (see e.g. A. Lempel and J. Ziv, “On the complexity of finite sequences,” IEEE Trans. Inform. Theory, vol. IT-22, pp. 75-81, 1976) is computed for each of the vital sign data streams 34 and for the drug administration data stream 36. This generates a Lempel-Ziv complexity metric 44 corresponding to each vital sign data stream 34, and a Lempel-Ziv complexity metric 46 corresponding to the drug administration data stream 36. The Lempel- Ziv complexity metrics 44, 46 are combined by an addition 50 (optionally with weighting of the data streams) or by another aggregation operator to generate an additive complexity value that is then thresholded by a thresholder 52 to generate a binary ALI indicator 54 having a positive (or other designated) value indicating the patient exhibits ALI or a negative (or other designated) value indicating the patient does not exhibit ALI.
  • With reference to FIG. 3, operation of the Lempel-Ziv complexity metric computation block 40 is further described. Lempel-Ziv complexity is used to quantify the complexity of different time series signals such as electroencephalography (EEG), heart rate, blood pressure, and so forth. In the system of FIG. 2, the input is a vital sign data stream 34 or the drug administration data stream 36. Lempel-Ziv (LZ) complexity is based on coarse-graining the data stream, i.e. discretizing the data stream in the time (if not already acquired as discrete samples) and value dimensions. In illustrative FIG. 3, the data stream is assumed to already be acquired as discrete time samples, and the value is coarse-grained by converting the numerical data into binary values, e.g “0” if the value is below a threshold Td or “1” if the value is above the threshold Td. Other coarsening approaches are contemplated, e.g. discretizing to a more granular sequence (0, 1, 2, . . . , N) using multiple thresholds. The output of this operation is the coarse-grained, e.g binary, data stream 60.
  • The LZ complexity is a measure of the amount of distinct patterns available in the sequence, or more particularly within a time interval or time window n of the sequence. In order to obtain the LZ complexity, the binary sequence 60 is scanned from left to right over the window n and a complexity counter is incremented by one unit every time a new (sub-)sequence of consecutive characters is encountered. In the illustrative example of FIG. 3, four sub-sequences 62 are identified in the window n, and thus the Lempel- Ziv complexity measure 44, 46 is in this case c(n)=4. Optionally, some normalization may be applied, e.g. so that the Lempel-Ziv complexity measure c(n) is expressed in units of new pattern occurrences per unit time. It will be appreciated that the processing shown diagrammatically in FIG. 3 may be repeated for successive (and optionally partially overlapping) time windows n to provide the Lempel-Ziv complexity measure c(n) as a function of (discretized) time.
  • With reference back to FIG. 2, and using the notation employed in FIG. 3, the adder 50 is suitably cHR(n)+cSBP(n)+cDBP (n)+cRR(n)+cDrugs(n). Alternatively, if weighting is employed the output may be written as wHRcHR(n)+wSBPcSBP(n)+wDBPcDBP(n)+wRRcRR(n)+wDrugscDrugs(n) where the w terms are scalar weights.
  • A Receiver Operating Characteristics (ROC) analysis is suitably used in order to obtain the optimal threshold Td of detection for use in the Lempel-Ziv (LZ) complexity measure computation of FIG. 3. In an actually-performed example, ROC analysis for LZ was performed on 506 ICU patients (training datasets), of which 206 where ALI-positive (i.e. exhibited ALI) and 300 were controls (i.e. ALI-negative, did not exhibit ALI). FIG. 4 shows the results for the training population, where the area under the ROC curve is 0.73 and the optimal threshold is 5.92 (sensitivity: 63% and specificity: 75%). The optimal threshold is marked by a black square in FIG. 4. To validate the approach, an ROC analysis was then performed on 6881 ICU patients (unseen test data). Out of these, 138 were ALI-positive and 6743 were controls. The threshold of 5.92 obtained with the training population was located in the ROC curve of testing datasets (also plotted in FIG. 4). The proposed approach achieved a better sensitivity (67%) and better specificity (76%) in the testing datasets. In these actually-performed examples, the summation 50 was unweighted (or, equivalently, all weights were w=1). If nonzero weights are to be employed, they can also be optimized during the training process.
  • With reference to FIG. 5, an embodiment employing logistic regression-based detection of ALI is described. This illustrative approach entails selecting the features of exploration, fitting a model to a training or derivation dataset of ICU patient data, and testing a model on a validation dataset, preferably one that reflects the true prevalence of ALI in the ICU population of interest.
  • The logistic regression model involves a nonlinear mapping of the independent or predictor variables such as heart rate (HR), respiratory rate (RR), non-invasive blood pressure measurement (NIBP-m), or so forth, to the dependent or response variable (e.g. ALI or control in the illustrative examples) through the logistic regression function or logit transformation. A suitable formulation is
  • p = β 0 + β 1 x 1 + β i x i 1 + β 0 + β 1 x 1 + β i x i
  • where p denotes the probability of ALI, β0 is a constant, and β1 . . . βi are coefficients of the predictors x1 . . . xi (e.g., the HR, RR, NIBP-m, et cetera). In a suitable approach, the logistic regression model is fit using the likelihood function L ({right arrow over (β)}, β0)=Πi=1 np({right arrow over (x)}i)y 1 (1−p({right arrow over (x)}i))1-y i where β0 is again a constant, {right arrow over (β)} is a vector of the coefficients of the predictors, p is again probability of ALI, and y is the true presence/absence of ALI. The coefficients are computed using minimization techniques such as the ordinary least squares (OLS) or the maximum likelihood estimator (MLE).
  • In an actually performed example, the logistic regression model used three features as input: HR, RR, and HR/NIBP-m, to yield a probability of ALI development. In the training phase, the constant β0 and coefficients {right arrow over (β)} were derived from a 600 patient dataset comprising 300 controls and 300 ALI patients using the foregoing equations. The model was applied continuously (in other words, applied to each unique time point for a patient) and a receiver operator characteristic (ROC) curve was drawn to determine the threshold providing the desired level of sensitivity and specificity. In the testing phase, the model was then applied in the same continuous manner to a validation set of unseen patient data comprising 6,690 controls and 326 ALI patients. An ROC curve was again drawn and the sensitivity and specificity at the previously determined threshold were compared to those obtained from the derivation dataset.
  • FIG. 5 shows the results. Performance of the logistic regression model on the training data resulted in 71.00% sensitivity and 74.33% specificity. Using the same threshold, performance of the model on the validation data resulted in 63.19% sensitivity and 81.05% specificity.
  • The actually performed example is merely illustrative. In general, higher or lower frequency data may be employed in the training, testing, and implementation of the logistic regression model. Other embodiments optionally include additional features, such as demographic and baseline health information, to the extent that such data is available via electronic medical records (EMRs) or other sources.
  • With reference to FIGS. 6 and 7, an embodiment employing log-likelihood ratio (LLR)-based detection of ALI is described. With particular reference to FIG. 6, a flowchart of a suitable log-likelihood ratio based detection of ALI is shown. Let N be the total number of patients in a derivation (i.e. training) data set, of which N1 have the disease (ALI in the illustrative example) and N0 do not have the disease. The disease state is denoted as D, i.e D=1 denotes ALI positive and D=0 denotes absence of ALI (i.e. ALI-negative). Let d=[d1 d2 . . . dL] denote a vector of patient data that is available to make a diagnosis. In illustrative FIG. 6 these L parameters include vital signs 70, e.g. RR, HR, FiO2 (fraction of inspired oxygen), PaO2 (partial pressure of oxygen in arterial blood), PEEP (positive end-expiratory pressure), or so forth, and laboratory test results 72, e.g. pH, Hgb (hemoglobin blood test result), or so forth. As another example (not illustrated), the L parameters may additionally or alternatively include data on whether the patient has one or more acute or chronic conditions such as pneumonia, diabetes, or so forth. The log-likelihood ratio is then defined as
  • L L R ( d _ ) = log [ p ( d _ / D = 1 ) p ( d _ / D = 0 ) ]
  • where p(d/D=1) is the joint probability distribution function of d given D=1 and p(d/D=0) is the joint probability distribution function of d given D=0. With the assumption that the L parameters are independent, the log-likelihood ratio can be rewritten as follows:
  • L L R ( d _ ) = log [ i = 1 L p ( d i / D = 1 ) i = 1 L p ( d i / D = 0 ) ] = i = 1 L log [ p ( d i / D = 1 ) p ( d i / D = 0 ) ] = i = 1 L L L R ( d i )
  • Thus, the joint log-likelihood ratio of all the parameters is the sum of the log-likelihood of the individual parameters.
  • FIG. 6 shows the testing phase. The log-likelihood ratio LLR(d) is computed in an operation 74 for a patient with input patient data vector d whose elements [d1 d2 . . . dL] store patient data for the patient under test. The ALI detection then proceeds using a threshold operation 76 as follows:
  • L L R ( d _ ) D = 0 D = 1 T
  • That is, if LLR(d)>T then the test result 78 is deemed ALI positive (D=1), whereas if LLR(d)<T then the test result 78 is deemed ALI negative (D=0). In these expressions, T is an optimum detection threshold determined from the training data set.
  • With reference to FIG. 7, results for an actually performed log-likelihood ratio-based ALI test are reported. An ROC analysis is used in order to obtain the optimal threshold T for the threshold operation 76. ROC analysis for LLR was performed on 506 ICU patients (training dataset), of which 206 where ALI and 300 were controls. The results of the training population are shown in FIG. 7. The area under the ROC curve is 0.88 and the optimal threshold is 2.6 (sensitivity: 86% and specificity: 77%). As more data sets are obtained for training the thresholds and performance values may change. The optimal threshold is marked as a black square in the plot. To validate the approach, an ROC analysis on 6881 ICU patients (unseen test data) was performed. Out of these, 138 were ALI and 6743 were controls. The threshold obtained from the training data is also shown in FIG. 7 in its corresponding location on the ROC curve generated from testing data. The approach achieved a specificity (84%) and sensitivity (72%) in the testing datasets. Location of the operating point (training threshold T) changed slightly in the testing datasets, with decreased sensitivity and increased specificity. However, the threshold is fairly robust considering the increased specificity. The approach also has an area under the ROC curve (0.86) for testing datasets very close to that of the training datasets (0.87) which is advantageous for reliable ALI detection.
  • The ALI/ARDS detection approaches employing a Lempel-Ziv complexity metric (LZ, described with reference to FIGS. 2-4), a logistic regression-based approach (LR, described with reference to FIG. 5), and a log-likelihood ratio-based approach (LLR, described with reference to FIG. 7) are illustrative examples, and other inference algorithms are contemplated. Such inference algorithms could include a fuzzy inference system, a Bayesian network, and a finite state machine, among others.
  • With reference to FIGS. 8-15, it is also contemplated to employ various aggregations of inference algorithms, and optionally other information, in detecting (i.e. inferring) the presence of ALI in a patient. The aggregation of such techniques leverages the observation made herein that each algorithm recognizes patterns in the data differently, so that an integrative (e.g. aggregative) approach using complementary information from various unique algorithms in combination is expected to give better performance than any one of the individual algorithms acting alone.
  • With particular reference to FIG. 8, a generic framework of the integrative approach is disclosed. The outputs of set of N algorithms 80, referred to herein without loss of generality as Algorithm 1, Algorithm 2, Algorithm 3, . . . , Algorithm N, are aggregated at an aggregation block 82 to generate an organ status indicator 84 that is suitably displayed and/or trended as a function of time on the bedside monitor 10, nurses' station monitoring device 20, (see FIG. 1) or so forth. The generic framework of FIG. 8 is not disease-specific.
  • With reference to FIG. 9, an application of the generic aggregation framework of FIG. 8 to ALI detection is shown. In this application the N algorithms 80 include six algorithms (i.e. N=6) as outlined in the following.
  • A first algorithm is based on a distillation of physicians' expertise. In illustrative FIG. 9, this is implemented as a fuzzy inference algorithm 90 that is built from linguistic (or fuzzy) information about relationships of variables and run using a set of decision rules 92 constructed based on clinical information 94 collected in discussions with physicians. The fuzzy inference algorithm 90 may, for example, constitute a clinical decision support system (CDSS) component.
  • A second algorithm is based on distillation of relevant clinical literature. In illustrative FIG. 9, this is implemented as a Bayesian network 100 that is structured from probabilities 102 computed based on clinical research 104. For example, a clinical study may indicate that statistically a combination of parameters is indicative of ALI with a probability P.
  • A third algorithm is based on the translation of pathophysiology in terms of causal relationships between variables (such as RR, HR, etc.). Potential causes of ALI development could be mechanical, chemical, or biological in nature. For instance, mechanical causes of ALI include fast/deep breathing and/or ventilation settings. Examples of mechanical conditions are:

  • Ventilation setting of positive end expiratory pressure (PEEP)<5  Condition 1:

  • PEEP>10  Condition 2:

  • plateau pressure>35 cmH2O.  Condition 3:
  • In illustrative FIG. 9, this is implemented as a state machine 110 implementing a logic flow 112 quantifying a clinical definition 114. In the instant case, if all of Conditions 1, 2, or 3 are not met, then the state machine 110 outputs ALI negative, while if any of the three conditions is met then the state machine 110 outputs ALI positive.
  • These first three algorithms are knowledge-based, and leverage clinical information, published clinical studies, and clinical definitions, respectively. The fourth, fifth, and sixth algorithms are data-based, and in illustrative FIG. 9 correspond to the LLR algorithm 120, LZ algorithm 130, and LR algorithm 140, respectively, described herein with reference to FIGS. 2-7. These algorithms 120, 130, 140 are based on ICU data 142 such as vitals, labs, and interventions (e.g. drug administration events), and are optionally also based on pre-ICU data 144 such as demographic data and/or known chronic diseases or conditions of the patient. (Note that the term “pre-ICU” indicates that such patient information are typically gathered prior to the patient being admitted to the ICU as part of the admissions procedures; however, the pre-ICU data 144 may in some cases be generated, in whole or in part, after the patient enters the ICU).
  • The aggregation block 82 may be implemented in various ways. In the illustrative ALI application of FIG. 9, the aggregation block 82 is implemented by linear discriminant analysis (LDA) or by a voting system (SOFALI). These illustrative aggregation approaches are described in turn in the following.
  • The linear discriminant function for each class k can be represented as:
  • y k ( x ) = - ( 1 2 ) μ k C - 1 μ k T + log ( p k ) + ( μ k T C - 1 ) x
  • where x are predictor variables (e.g., the different ALI detection algorithms), pk are the prior probabilities of classes k, and C is the pooled covariance matrix across classes. For the illustrative ALI detection application, the LDA coefficients are obtained for the different predictor variables (i.e., different algorithms) on the training data set. LDA coefficients are then suitably passed through a softmax transformation in order to convert the coefficients to probabilities pk according to:
  • p k = exp ( y k ) j = 1 k exp ( y j )
  • The voting system aggregator is suitably implemented as follows. The thresholds of the knowledge-based and data-based approaches are obtained from the training data set. These individual thresholds are then used to obtain a voting system based ALI detection (based on the number of algorithms detecting ALI). TABLE 1 shows the illustrative voting system (SOFALI) employed for integrating the six different algorithms of illustrative FIG. 9.
  • TABLE 1
    Voting system for integrating the
    different ALI detection algorithms
    Number of algorithms detecting ALI Votes (SOFALI)
    Any one or none 0
    2 1
    3 2
    4 3
    5 or 6 4

    Other embodiments could include a scale of 0 to 1 where the number of votes is normalized by the total number of algorithms present.
  • In an actually performed implementation, all of the knowledge-based and data-based and integrative approaches of the illustrative aggregative ALI detection system of FIG. 9 were trained using 506 ICU patient data and validated on an unseen 6881 ICU patient data. Receiver Operating Characteristics curve (ROC) were used to assess the performance of the different approaches. An ROC analysis was used in order to obtain the optimal threshold of ALI detection. ROC analysis for the all different approaches was performed on 506 ICU patients (training datasets), of which 206 where ALI and 300 were controls. The results of the training population are shown in FIG. 10. The optimal threshold for each integrative approach is represented with an asterisk (*) in FIG. 10. The thresholds corresponding to these asterisks are 0.859 for LDA and 2 for SOFALI.
  • In order to validate the two aggregation approaches, an ROC analysis on 6881 ICU patients (unseen test data) was performed. Out of these, 138 were ALI and 6743 were controls. The thresholds obtained from the training data for LDA and SOFALI respectively and shown in the ROC curve obtained from validation data FIG. 11, change position slightly, with decreased sensitivity and increased specificity, indicating that the threshold is fairly robust. The proposed approaches achieved a better specificity in the testing datasets which is valuable in the context of a reliable ALI detection.
  • With reference to FIGS. 12 and 13, trajectories of the integrative LDA approach are shown for an illustrative ALI patient (FIG. 12) and for a control patient (FIG. 13). With reference to FIGS. 14 and 15, trajectories of the integrative SOFALI approach are shown for an illustrative ALI patient (FIG. 14) and for a control patient (FIG. 15). FIGS. 12-15 demonstrate that both the LDA and SOFALI integrative approaches detected ALI early as compared to the retrospectively determined ALI onset time by the physician.
  • The aggregation embodiment described with reference to FIG. 9 is merely illustrative, and numerous variants are contemplated. For example, the set of algorithms can be different from the illustrative six algorithms of FIG. 9. Aggregation algorithms other than LDA or SOFALI are also contemplated, such as aggregation based on a distance metric or based on decision trees or so forth. Moreover, while the illustrative embodiments relate to detection of ALI/ARDS, it will be appreciated that analogous approaches can be employed to detect other illnesses or conditions such as Acute Kidney Injury (AKI), Disseminated Intravascular coagulation (DIC), using suitable vital signs and optionally other features such as the illustrative drug administration data stream, and training on suitable training data sets to optimize the inference algorithm parameters.
  • The ALI status indicator computed by any of the disclosed algorithms (with or without aggregation) may be utilized in various ways. In the illustrative example, the ALI status indicator may be displayed and optionally logged on the bedside monitor 10 and/or displayed and optionally logged at the nurses' station electronic monitoring device 20 (see FIG. 1). The display can be numeric, and/or in the form of a trend line plotting ALI status indicator value versus time. In the case of an inference engine that generates a value that is thresholded to generate an ALI positive (or negative) indication, it is contemplated to additionally or alternatively display the value without thresholding. For example, the ALI value generated by the inference engine may be plotted as a trend line with the ALI positive/negative threshold shown as a horizontal line superimposed on the trend line graph. Additionally or alternatively, multiple thresholds may be applied to correspond to increasing disease severity or increasing probability of ARDS. Color coding can be applied to indicate the level of severity of the threshold.
  • Additionally or alternatively, the ALI status indicator can serve as input to a clinical decision support system (CDSS), serving as one piece of data used in conjunction with other data in generating clinical recommendations for consideration by the physician.
  • In these various applications, the ALI status indicator is typically not accepted as a diagnosis, but rather the ALI status indicator serves as one piece of data for consideration by the patient's physician or other expert medical personnel in deciding the most appropriate course of treatment for the patient.
  • A typical ICU services several patients at any given time. Each of these patients may (at least in general) be susceptible to ALI/ARDS, and is advantageously monitored for this condition using techniques disclosed herein. However, the ICU is a stressful and complex environment, and additional information such as a set of ALI status indicators for the patients in the ICU may contribute to information overload. In view of this, it is further disclosed herein to provide a multi-patient monitoring display that facilitates rapid review of the condition of all patients in the ICU being monitored for ALI. This multi-patient monitoring display is suitably employed at the nurses' station electronic monitoring device 20 (see FIG. 1) to provide monitoring of all patients under the care of the nurse or nurses (or other medical personnel) assigned to the nurses' station.
  • With reference to FIG. 16, an illustrative overview multi-patient monitoring display 200 is suitably shown on the nurses' station electronic monitoring device 20 of FIG. 1. The illustrative overview display 200 diagrammatically represents each patient in the current ICU (the medical ICU, i.e. MICU, in illustrative FIG. 16) by a box containing the most pertinent information, in the illustrative example including the patient identification (PID) number and the ALI status indicator value for the patient, represented in illustrative FIG. 16 by the SOFALI aggregation value (more generally, any of the ALI status indicators disclosed herein, with or without aggregation, may be employed). Optionally, the boxes diagrammatically representing the patients are laid out on the display 200 in a manner mimicking the physical layout of the patients in the ICU. In illustrative FIG. 200 the illustrative MICU has ten beds laid out in a “C” pattern and all ten beds are occupied by patients. If a bed was unoccupied, this could be suitably represented by employing an empty box for that bed or by omitting the representative box entirely.
  • To further facilitate rapid assessment of patient condition, each of the diagrammatic boxes is optionally color-coded to represent the ALI status of the patient. In illustrative FIG. 16, the color coding is diagrammatically represented by different cross-hatchings, with patients having SOFALI index values 0 or 1 being one color (e.g. green or white or no color), patients having SOFALI index values 2 or 3 being a different color (e.g. yellow to indicate a “watch” status for these patients), and patients having SOFALI of 4 (or possibly greater) being yet a different color (e.g. red to indicate a serious ALI or ARDS condition). Alternatively, the color-coding can correspond to severity of illness and a change in color can correspond to a new threshold or boundary of a score ranging. For example, for a score ranging from 0 to 100, 0 to 50 can represent a low risk group, 50 to 75 can indicate a medium risk (“watch” or “warning”) group, and above 75 can indicate a high risk group. With brief reference to FIG. 17, the overview display 200 optionally includes a drop-down menu 202 or other graphical user interface (GUI) dialog enabling a nurse or other operator to switch to a different ICU unit.
  • The information contained in the diagrammatic boxes of the overview display 200 is merely an illustrative example, and additional or other information may be shown. For example, patients may be identified by name instead of or in addition to by PID number. Other serious conditions may be indicated instead of or in addition to ALI. If two or more conditions are indicated and are to be represented by color coding, the color coding may be shown in different areas of the box, or the entire box may be color coded by the color representing the most serious condition (e.g. “red” if any represented condition has a “red” status color, even if some other displayed condition would be “yellow” or “white”).
  • In various embodiments, the multi-patient overview display provides a quick “snapshot” overview of critical health status of a group of patients in the ICU, or in other locales (e.g. ED, OR, ward, etc.), via diagrammatic health status blocks. In various embodiments, one or more of the following may be incorporated: (1) individual color-coded block with numeric value and label (e.g. overall health); (2) individual color-coded block with numeric value and label (e.g. ALI health); (3) Multiple color-coded blocks contained within a single block with numeric values and labels (e.g. acute lung injury, acute kidney injury, disseminated intravascular coagulation, acute myocardial infarction, et cetera); or so forth. In general, each diagrammatic block of the overview display provides an overall view of critical illness status of an individual patient, and the collection of blocks in the overview display thus provides this information for all patients in the ICU.
  • With reference to FIGS. 18 and 19, by selecting the diagrammatic box representing a particular patient, for example by clicking on the box using a mouse or other pointing device, touching the box in the case of a touchscreen, or so forth, a zoomed-in view of the status of the selected patient is shown in a zoomed-in patient display 210 (FIG. 18) or alternative-embodiment zoomed-in patient display 220 (FIG. 19). In various embodiments the zoomed-in display shows a view of ALI/ARDS development (and/or development of another monitored condition), in time, for an individual patient. Optionally, the zoomed-in display may show predicted development in a given number of hours in the future. An ALI status indicator may be displayed as a value (optionally quantized) and corresponding color for all organ health assessment scores used in the ICUs (e.g. SOFA, AKIN criteria, et cetera, other contemplated scores including by way of illustrative example quantized CDS indicators for ALI, AKI, et cetera) in one concise, easy to read “snapshot” display. Trend indicators may be shown in various formats, such as using+/−signs, or up, down, horizontal arrows, by various color coding schemes (solid: traffic light pattern; spectrum-like: heatmap pattern; or so forth), by positive/negative numerical values, increased/decreased position on a vertical axis, or so forth. The combination of the overview display and the patient-specific zoom-in display provides a quick and easy mechanism for changing views/interfaces for groups of patients or individual patients and enables focusing on ALI or another organ system or syndrome of interest.
  • It is contemplated to enable customization of patient groups, organs/syndromes of interest, or scores used to represent a particular organ's health (e.g. RIFLE vs. AKIN criteria vs. CDS AKI indicator). Optionally, CDSS capability is incorporated to aid in decision making via display of suggested/recommended algorithm decision thresholds and in other embodiments, confidence intervals or bounds on this decision threshold.
  • In embodiments employing aggregation as previously described with reference to FIGS. 8 and 9, the zoomed-in view optionally shows results of constituent algorithms of the aggregation, optionally trended in time, that contribute to the aggregated algorithm output. While rectangular diagrammatic boxes are illustrated, markers used for organ health status can be of other shapes and of various sizes (e.g. actual traffic light, speedometer, or organ shape/image that changes color).
  • Current and recent past organ health information may be visualized via functionality including (by way of illustrative example): plotting; re-plotting from different starting points; animated plotting; pausing/resuming simulations; zooming (e.g. one-hour trends instead of six-hour trends); and so forth. In some embodiments, age of information, new or (carried) zero order held values, can be depicted via mechanisms such as filled/unfilled markers, outlined/not outlined markers, bolded/not bolded marker outlines, and so forth.
  • Without limiting the foregoing, the illustrative examples of FIGS. 16-19 are described in further detail in the following.
  • With reference to FIG. 16, a group overview display 200 is shown for an MICU including ten beds all occupied by patients. If a bed is empty, the text might say “Bed Empty”, the color might be light gray or faded, the action functions of the block are disabled, etc. If a bed is occupied, the block is labeled with a patient identifier (e.g. PID 123456). The text also includes a label and numeric value for the score of the organ indicator (e.g. ALI indicator SOFALI indicating severity of the ALI). Green, yellow, and red indicate low, medium, and high risk of ALI, respectively. In other embodiments, the color can be a spectrum of colors from lighter to darker hues. In still other embodiments, the color and score may indicate an overall organ health (e.g. respiratory, cardiovascular, renal, etc.). In still yet other embodiments, the scores for other organs can also be depicted. When multiple conditions are to be color-coded, the block is optionally segmented or has several components for each organ system, where each has the respective color and score indicating that organ's health.
  • With reference to FIG. 17, the overview display 200 of FIG. 16 is interacted with by a nurse to select another ICU (e.g. medical, surgical, trauma, etc.) via the drop-down GUI dialog 202. Instead of representing a specific ICU, additional groups of patients might include Worst10 (e.g. display the 10 most critically ill patients in all ICU's of the hospital or other medical center). User groups and number of beds (thus patients displayed) are as appropriate for the given ICU, and may be configurable for example using a “drag-and-drop” user interface by which a user drags a new bed into the ICU display and links it with a set of input data streams for that bed. (Similarly a bed can be removed by dragging it off the display).
  • In a contemplated variant embodiment of the overview display (not shown), the color coding conveys different information, namely being used to identify changes in parameters. For example, if a patient's organ status is declining, this can be reflected by “red” color coding even if the actual level of the ALI or other organ status indicator is not indicating ALI positive in this embodiment the color coding highlights changes rather than absolute values of organ status indicators.
  • With reference to FIG. 18, a zoomed-in display 210 is shown, which is suitably generated by the nurse selecting (e.g. clicking or double-clicking with a mouse, or touching in the case of a touchscreen) one diagrammatic box of the overview display 200 of FIG. 16 to select an individual patient to which to zoom. The illustrative patient of FIG. 18 has a high risk of ALI. Demographics are displayed in the upper right of the display 210. Demographics include but are not limited to height, weight, age, gender, predicted body weight, body mass index (BMI), hospital or ICU admission or discharge dates and times, chronic conditions, reasons for admissions, current diagnoses, and so forth. The upper left plot of the display 210 shows current and predicted ALI CDS algorithm output (aggregate SOFALI score on vertical axis, time on horizontal axis). The six lower left plots of the display 210 respectively plot each of the six individual algorithms that are aggregated to obtain the SOFALI score (cf. FIG. 9). For the plots in the lower left of each of the individual algorithms and the aggregated plot in the upper left, the recommended decision threshold (and optionally its confidence bounds) are optionally displayed as a line of value y on the vertical axis that spans the horizontal axis. The nurse or other user can select to review a new patient by using the drop down GUI dialog box in the uppermost left. The lower right side of the display shows a matrix of organ system health (SOFALI, cardiovascular, respiratory, renal, hepatic, coagulation) via colored markers over time (different colors are diagrammatically indicated in FIG. 18 by different shading levels). Markers could be different sizes, shapes, or images, can have bolded/non-bolded outlines to distinguish new values from old or carried values, and/or can increase or decrease in position on the vertical axis to represent increases and decreases in scores. Other embodiments could incorporate other clinical assessments (SOFA, AKIN, SIRS, etc.) or newly developed CDS assessments (CDS for ALI, AKI, DIC, etc.) or a combination of both. Selection of scores to be used or displayed is optionally customizable in a selectable preferences, configuration, or set-up window (not shown). In other embodiments, the focus organ system or the left side of the display can be changed to other organ systems by selecting a new organ to display. In other embodiments, a group or patient group (similar to or some version of figures above) may be displayed in the place of the individual algorithms. In some embodiments the nurse or other use can press a play button to animate plots and review patient health trends and trajectories over time from the start time or a selected time to the current time. Optional pause/resume functionality allows further analysis of particular points of concern. User interfacing for such controls is suitably implemented by user-controllable time slider bars or the like.
  • With reference to FIG. 19, an alternative embodiment zoomed-in display 220 is shown, in which the matrix of organ system health in the lower right side of the display is modified to employ a grid with numeric values in the grid cells. The organ system overview on the right side of the GUI includes the color-coding system as previously described (traffic light or spectrum-like, again diagrammatically represented in FIG. 19 by different shading levels). The color represents the current score, though other embodiments may include a numeric value for the current score as well. The “+/−” signs indicate a positive or negative trend from the previous value, where the higher or more positive the SOFA and SOFALI value, the worse the organ health. The numeric value immediately following a “+/−” sign is the delta or change from the previous value. Future embodiments can incorporate combinations of these current values and delta values or can use directional arrows instead of “+/−” signs.
  • With returning reference to FIG. 1, the disclosed techniques for detecting ALI or other conditions of concern for ICU patients are suitably implemented by the built-in computer, microprocessor, or so forth of the illustrative bedside monitor 10 and/or of the illustrative nurses' station electronic monitoring device 20. It will also be appreciated that the disclosed techniques can be embodied by a non-transitory storage medium storing instructions executable by such an electronic data processing device to perform the disclosed detection methods. The non-transitory storage medium may, for example, comprise a hard disk or other magnetic storage medium, random access memory (RAM), read-only memory (ROM), or another electronic storage medium, an optical disk or other optical storage medium, a combination of the foregoing, or so forth.
  • The invention has been described with reference to the preferred embodiments. Obviously, modifications and alterations will occur to others upon reading and understanding the preceding detailed description. It is intended that the invention be construed as including all such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims (31)

1. A non-transitory storage medium storing instructions executable by an electronic data processing device including a display to monitor a patient for acute lung injury (ALI) by operations including:
(i) receiving values of a plurality of physiological parameters for the patient;
(ii) receiving drug administration information pertaining to administration of one or more drugs to the patient;
(iii) computing an ALI indicator value based at least on the received values of the plurality of physiological parameters for the patient and the received drug administration information; and
(iv) displaying a representation of the computed ALI indicator value on the display.
2. (canceled)
3. (canceled)
4. The non-transitory storage medium of claim 1 wherein:
the receiving comprises receiving a data stream of values for the patient for each physiological parameter of the plurality of physiological parameters,
the computing comprises computing the ALI indicator value as a function of time based on the received data streams of values for the patient, and
the displaying comprises displaying a trend line representing the computed ALI indicator value as a function of time.
5. (canceled)
6. (canceled)
7. (canceled)
8. The non-transitory storage medium of claim 1 wherein:
the receiving comprises receiving a data stream of values for the patient for each physiological parameter of the plurality of physiological parameters, and
the computing comprises (1) computing a Lempel-Ziv complexity metric for each received data stream of values for the patient and (2) computing an aggregation of the Lempel-Ziv complexity metrics, the ALI indicator value being based at least on the aggregation of the Lempel-Ziv complexity metrics.
9. The non-transitory storage medium of claim 1 wherein the computing comprises:
computing the ALI indicator value based at least in part on applying a logistic regression model to the received values of the plurality of physiological parameters for the patient.
10. The non-transitory storage medium of claim 1 wherein the computing comprises:
computing the ALI indicator value based at least in part on applying a log-likelihood ratio (LLR) model to the received values of the plurality of physiological parameters for the patient.
11. The non-transitory storage medium of claim 1 wherein the computing comprises:
computing the ALI indicator value based at least in part on applying a trained model to the received values of the plurality of physiological parameters for the patient, the trained model having one or more model parameters trained on a training set comprising reference patients to distinguish between reference patients labeled ALI-positive and ALI-negative.
12. The non-transitory storage medium of claim 11 wherein the trained model comprises a Lempel-Ziv complexity metric model and the parameters include a threshold.
13. The non-transitory storage medium of claim 11 wherein the trained model comprises a logistic regression model and the parameters include coefficients βi scaling respective received values xi of the plurality of physiological parameters for the patient in the logistic regression model.
14. The non-transitory storage medium of claim 11 wherein the trained model comprises a log-likelihood ratio (LLR) model and the parameters include joint probabilities of received values di of the plurality of physiological parameters given ALI-positive and joint probabilities of received values di given ALI-negative.
15. The non-transitory storage medium of claim 1 wherein the computing comprises:
computing algorithm ALI indicator values for a plurality of different inference algorithms trained to discriminate between ALI-positive and ALI-negative patients; and
computing the ALI indicator value as an aggregation of the algorithm ALI indicator values.
16. The non-transitory storage medium of claim 15 wherein the computing of the ALI indicator value as an aggregation of the algorithm ALI indicator values comprises:
computing the ALI indicator value by applying linear discriminant analysis (LDA) to the algorithm ALI indicator values.
17. The non-transitory storage medium of claim 15 wherein the computing of the ALI indicator value as an aggregation of the algorithm ALI indicator values comprises:
computing the ALI indicator value by applying a voting analysis to the algorithm ALI indicator values.
18. The non-transitory storage medium of claim 1 further storing instructions executable by the electronic data processing device including the display to monitor a plurality of patients in an Intensive Care Unit (ICU) for ALI by operations including:
performing the operations (i) and (ii) for each patient to generate an ALI indicator value for each patient;
wherein the displaying operation (iii) comprises simultaneously displaying on the display a diagrammatic representation of each patient, the diagrammatic representation of each patient including an identification of the patient and a representation of the ALI indicator value for the patient.
19. (canceled)
20. An apparatus comprising:
an electronic data processing device including a display; and
a non-transitory storage medium as set forth in claim 1 operatively connected with the electronic data processing device to execute the instructions stored on the non-transitory storage medium to monitor a patient for acute lung injury (ALI).
21. (canceled)
22. (canceled)
23. A method comprising:
receiving values of a plurality of physiological parameters for a patient in an intensive care unit (ICU) at an electronic data processing device including a display;
receiving drug administration information pertaining to administration of one or more drugs to the patient;
using the electronic data processing device, computing an ALI indicator value (54, 78, 84) based at least on the received values of the plurality of physiological parameters for the patient and the received drug administration information using an inference algorithm trained on a training set comprising reference patients to distinguish between reference patients having ALI and reference patients not having ALI; and
displaying a representation of the computed indicator value on the display of the electronic data processing device.
24. (canceled)
25. (canceled)
26. (canceled)
27. (canceled)
28. (canceled)
29. (canceled)
30. (canceled)
31. (canceled)
US14/379,376 2012-02-17 2013-02-14 Acute lung injury (ali)/acute respiratory distress syndrome (ards) assessment and monitoring Abandoned US20150025405A1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US14/379,376 US20150025405A1 (en) 2012-02-17 2013-02-14 Acute lung injury (ali)/acute respiratory distress syndrome (ards) assessment and monitoring

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US201261600308P 2012-02-17 2012-02-17
US201361762988P 2013-02-11 2013-02-11
US14/379,376 US20150025405A1 (en) 2012-02-17 2013-02-14 Acute lung injury (ali)/acute respiratory distress syndrome (ards) assessment and monitoring
PCT/IB2013/051201 WO2013121374A2 (en) 2012-02-17 2013-02-14 Acute lung injury (ali)/acute respiratory distress syndrome (ards) assessment and monitoring

Publications (1)

Publication Number Publication Date
US20150025405A1 true US20150025405A1 (en) 2015-01-22

Family

ID=48095950

Family Applications (1)

Application Number Title Priority Date Filing Date
US14/379,376 Abandoned US20150025405A1 (en) 2012-02-17 2013-02-14 Acute lung injury (ali)/acute respiratory distress syndrome (ards) assessment and monitoring

Country Status (7)

Country Link
US (1) US20150025405A1 (en)
EP (1) EP2815343A2 (en)
JP (3) JP6215845B2 (en)
CN (1) CN104115150B (en)
BR (1) BR112014020040A8 (en)
RU (1) RU2629799C2 (en)
WO (1) WO2013121374A2 (en)

Cited By (68)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170068423A1 (en) * 2015-09-08 2017-03-09 Apple Inc. Intelligent automated assistant in a media environment
JP2018032394A (en) * 2016-08-25 2018-03-01 株式会社日立製作所 Device control based on hierarchical data
WO2018162644A1 (en) * 2017-03-10 2018-09-13 Koninklijke Philips N.V. Patient status monitor with visually strong patient status display
CN108702539A (en) * 2015-09-08 2018-10-23 苹果公司 Intelligent automation assistant for media research and playback
US10528367B1 (en) * 2016-09-02 2020-01-07 Intuit Inc. Execution of workflows in distributed systems
WO2020216437A1 (en) * 2019-04-23 2020-10-29 Espire Technologies Gmbh Device and method for localising or identifying malignancies
US10825167B2 (en) 2017-04-28 2020-11-03 Siemens Healthcare Gmbh Rapid assessment and outcome analysis for medical patients
US10978090B2 (en) 2013-02-07 2021-04-13 Apple Inc. Voice trigger for a digital assistant
US10984798B2 (en) 2018-06-01 2021-04-20 Apple Inc. Voice interaction at a primary device to access call functionality of a companion device
US10998095B2 (en) * 2015-04-08 2021-05-04 Koninklijke Philips N.V. Tool for recommendation of ventilation therapy guided by risk score for acute respirator distress syndrome (ARDS)
US11009970B2 (en) 2018-06-01 2021-05-18 Apple Inc. Attention aware virtual assistant dismissal
US11037565B2 (en) 2016-06-10 2021-06-15 Apple Inc. Intelligent digital assistant in a multi-tasking environment
US11070949B2 (en) 2015-05-27 2021-07-20 Apple Inc. Systems and methods for proactively identifying and surfacing relevant content on an electronic device with a touch-sensitive display
US11087759B2 (en) 2015-03-08 2021-08-10 Apple Inc. Virtual assistant activation
US11120372B2 (en) 2011-06-03 2021-09-14 Apple Inc. Performing actions associated with task items that represent tasks to perform
US11126400B2 (en) 2015-09-08 2021-09-21 Apple Inc. Zero latency digital assistant
US11133008B2 (en) 2014-05-30 2021-09-28 Apple Inc. Reducing the need for manual start/end-pointing and trigger phrases
US11152002B2 (en) 2016-06-11 2021-10-19 Apple Inc. Application integration with a digital assistant
US11169616B2 (en) 2018-05-07 2021-11-09 Apple Inc. Raise to speak
US11237797B2 (en) 2019-05-31 2022-02-01 Apple Inc. User activity shortcut suggestions
US11257504B2 (en) 2014-05-30 2022-02-22 Apple Inc. Intelligent assistant for home automation
US11321116B2 (en) 2012-05-15 2022-05-03 Apple Inc. Systems and methods for integrating third party services with a digital assistant
US11348582B2 (en) 2008-10-02 2022-05-31 Apple Inc. Electronic devices with voice command and contextual data processing capabilities
US11380310B2 (en) 2017-05-12 2022-07-05 Apple Inc. Low-latency intelligent automated assistant
US11388291B2 (en) 2013-03-14 2022-07-12 Apple Inc. System and method for processing voicemail
US11405466B2 (en) 2017-05-12 2022-08-02 Apple Inc. Synchronization and task delegation of a digital assistant
US11423886B2 (en) 2010-01-18 2022-08-23 Apple Inc. Task flow identification based on user intent
US11431642B2 (en) 2018-06-01 2022-08-30 Apple Inc. Variable latency device coordination
EP4068305A1 (en) * 2021-03-31 2022-10-05 Riatlas S.r.l. Method for displaying on a screen of a computerized apparatus a temporal trend of a state of health of a patient and computerized apparatus
US11467802B2 (en) 2017-05-11 2022-10-11 Apple Inc. Maintaining privacy of personal information
US11500672B2 (en) 2015-09-08 2022-11-15 Apple Inc. Distributed personal assistant
US11516537B2 (en) 2014-06-30 2022-11-29 Apple Inc. Intelligent automated assistant for TV user interactions
US11526368B2 (en) 2015-11-06 2022-12-13 Apple Inc. Intelligent automated assistant in a messaging environment
US11532306B2 (en) 2017-05-16 2022-12-20 Apple Inc. Detecting a trigger of a digital assistant
US11580990B2 (en) 2017-05-12 2023-02-14 Apple Inc. User-specific acoustic models
US11599331B2 (en) 2017-05-11 2023-03-07 Apple Inc. Maintaining privacy of personal information
IT202100028643A1 (en) * 2021-11-11 2023-05-11 Riatlas S R L Method of changing a display on a computerized apparatus screen of a health condition of a patient and computerized apparatus
US11657813B2 (en) 2019-05-31 2023-05-23 Apple Inc. Voice identification in digital assistant systems
US11671920B2 (en) 2007-04-03 2023-06-06 Apple Inc. Method and system for operating a multifunction portable electronic device using voice-activation
US11670289B2 (en) 2014-05-30 2023-06-06 Apple Inc. Multi-command single utterance input method
US11675491B2 (en) 2019-05-06 2023-06-13 Apple Inc. User configurable task triggers
US11675829B2 (en) 2017-05-16 2023-06-13 Apple Inc. Intelligent automated assistant for media exploration
US20230207083A1 (en) * 2020-05-27 2023-06-29 Koninklijke Philips N.V. Methods and systems for individualized risk score analysis
US11696060B2 (en) 2020-07-21 2023-07-04 Apple Inc. User identification using headphones
US11705130B2 (en) 2019-05-06 2023-07-18 Apple Inc. Spoken notifications
US11710482B2 (en) 2018-03-26 2023-07-25 Apple Inc. Natural assistant interaction
US11727219B2 (en) 2013-06-09 2023-08-15 Apple Inc. System and method for inferring user intent from speech inputs
US11755276B2 (en) 2020-05-12 2023-09-12 Apple Inc. Reducing description length based on confidence
US11765209B2 (en) 2020-05-11 2023-09-19 Apple Inc. Digital assistant hardware abstraction
US11783815B2 (en) 2019-03-18 2023-10-10 Apple Inc. Multimodality in digital assistant systems
US11790914B2 (en) 2019-06-01 2023-10-17 Apple Inc. Methods and user interfaces for voice-based control of electronic devices
US11798547B2 (en) 2013-03-15 2023-10-24 Apple Inc. Voice activated device for use with a voice-based digital assistant
US11809783B2 (en) 2016-06-11 2023-11-07 Apple Inc. Intelligent device arbitration and control
US11838734B2 (en) 2020-07-20 2023-12-05 Apple Inc. Multi-device audio adjustment coordination
US11853647B2 (en) 2015-12-23 2023-12-26 Apple Inc. Proactive assistance based on dialog communication between devices
US11854539B2 (en) 2018-05-07 2023-12-26 Apple Inc. Intelligent automated assistant for delivering content from user experiences
US11888791B2 (en) 2019-05-21 2024-01-30 Apple Inc. Providing message response suggestions
US11886805B2 (en) 2015-11-09 2024-01-30 Apple Inc. Unconventional virtual assistant interactions
US11893992B2 (en) 2018-09-28 2024-02-06 Apple Inc. Multi-modal inputs for voice commands
US11914848B2 (en) 2020-05-11 2024-02-27 Apple Inc. Providing relevant data items based on context
US11947873B2 (en) 2015-06-29 2024-04-02 Apple Inc. Virtual assistant for media playback
US12001933B2 (en) 2015-05-15 2024-06-04 Apple Inc. Virtual assistant in a communication session
US12010262B2 (en) 2013-08-06 2024-06-11 Apple Inc. Auto-activating smart responses based on activities from remote devices
US12014118B2 (en) 2017-05-15 2024-06-18 Apple Inc. Multi-modal interfaces having selection disambiguation and text modification capability
US12051413B2 (en) 2015-09-30 2024-07-30 Apple Inc. Intelligent device identification
US12067985B2 (en) 2018-06-01 2024-08-20 Apple Inc. Virtual assistant operations in multi-device environments
US12073147B2 (en) 2013-06-09 2024-08-27 Apple Inc. Device, method, and graphical user interface for enabling conversation persistence across two or more instances of a digital assistant
US12087308B2 (en) 2010-01-18 2024-09-10 Apple Inc. Intelligent automated assistant

Families Citing this family (21)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140244304A1 (en) * 2013-02-28 2014-08-28 Lawrence A. Lynn System and Method for Generating Quaternary Images of Biologic Force Propagation and Recovery
EP3107448B1 (en) 2014-02-19 2019-10-02 Koninklijke Philips N.V. Method of detecting ards and systems for detecting ards
US20170017767A1 (en) * 2014-03-13 2017-01-19 Koninklijke Philips N.V. Patient watch-dog and intervention/event timeline
US10366206B2 (en) * 2014-12-04 2019-07-30 Koninklijke Philips N.V. System and method for providing connecting relationships between wearable devices
CN104899415B (en) * 2015-04-23 2018-05-18 张姬娟 Method for information display and system
JP6545591B2 (en) * 2015-09-28 2019-07-17 富士フイルム富山化学株式会社 Diagnosis support apparatus, method and computer program
WO2017055949A1 (en) 2015-09-28 2017-04-06 Koninklijke Philips N.V. Clinical decision support for differential diagnosis of pulmonary edema in critically ill patients
US20180322951A1 (en) * 2015-11-03 2018-11-08 Koninklijke Philips N.V. Prediction of acute respiratory disease syndrome (ards) based on patients' physiological responses
EP3404666A3 (en) * 2017-04-28 2019-01-23 Siemens Healthcare GmbH Rapid assessment and outcome analysis for medical patients
WO2019020497A1 (en) * 2017-07-25 2019-01-31 Koninklijke Philips N.V. Contextualized patient-specific presentation of prediction score information
CN109886411B (en) * 2019-02-25 2021-05-07 浙江远图互联科技股份有限公司 Rule base representation and inference method of pressure injury clinical decision system
US12002203B2 (en) 2019-03-12 2024-06-04 Bayer Healthcare Llc Systems and methods for assessing a likelihood of CTEPH and identifying characteristics indicative thereof
KR102251478B1 (en) 2019-03-28 2021-05-12 가톨릭대학교 산학협력단 Method and system for detecting wheeze sound based on artificial intelligence
CA3154689A1 (en) 2019-09-18 2021-03-25 Bayer Aktiengesellschaft System, method, and computer program product for predicting, anticipating, and/or assessing tissue characteristics
AU2020347797A1 (en) 2019-09-18 2022-03-31 Bayer Aktiengesellschaft Forecast of MRI images by means of a forecast model trained by supervised learning
WO2021110446A1 (en) 2019-12-05 2021-06-10 Bayer Aktiengesellschaft Assistance in the detection of pulmonary diseases
CN111657888A (en) * 2020-05-28 2020-09-15 首都医科大学附属北京天坛医院 Severe acute respiratory distress syndrome early warning method and system
RU2740115C1 (en) * 2020-06-15 2021-01-11 Сергей Анатольевич Точило Method of instant diagnostics of respiratory failure
CN112932458A (en) * 2021-01-26 2021-06-11 青岛百洋智能科技股份有限公司 Clinical intelligent aid decision-making method and system for acute respiratory distress syndrome
CN114098638B (en) * 2021-11-12 2023-09-08 马欣宇 Interpretable dynamic disease severity prediction method
WO2023175059A1 (en) * 2022-03-17 2023-09-21 Koninklijke Philips N.V. Predicting and stratififying acute respiratory distress syndrome

Family Cites Families (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
RU2085114C1 (en) * 1994-07-07 1997-07-27 Государственный научно-исследовательский институт экстремальной медицины, полевой фармации и медицинской техники Министерства обороны РФ Device for urgent medical sorting of victims
US5724983A (en) * 1994-08-01 1998-03-10 New England Center Hospitals, Inc. Continuous monitoring using a predictive instrument
US6067466A (en) * 1998-11-18 2000-05-23 New England Medical Center Hospitals, Inc. Diagnostic tool using a predictive instrument
US7117108B2 (en) * 2003-05-28 2006-10-03 Paul Ernest Rapp System and method for categorical analysis of time dependent dynamic processes
JP2007512588A (en) * 2003-10-29 2007-05-17 ノボ・ノルデイスク・エー/エス Medical advice system
US20070118054A1 (en) * 2005-11-01 2007-05-24 Earlysense Ltd. Methods and systems for monitoring patients for clinical episodes
US9820658B2 (en) * 2006-06-30 2017-11-21 Bao Q. Tran Systems and methods for providing interoperability among healthcare devices
JP2006255134A (en) * 2005-03-17 2006-09-28 Ikeda Denshi Kogaku Kenkyusho:Kk Brain wave measurement/display method and device
EP1910958A2 (en) * 2005-06-08 2008-04-16 Mediqual System and method for dynamic determination of disease prognosis
CN101365373A (en) * 2005-06-21 2009-02-11 早期感知有限公司 Techniques for prediction and monitoring of clinical episodes
EP1903932B1 (en) * 2005-06-22 2010-12-22 Koninklijke Philips Electronics N.V. An apparatus to measure the instantaneous patients' acuity value
US8100829B2 (en) * 2006-10-13 2012-01-24 Rothman Healthcare Corporation System and method for providing a health score for a patient
JP2008176473A (en) * 2007-01-17 2008-07-31 Toshiba Corp Patient condition variation predicting device and patient condition variation-managing system
JP5159242B2 (en) * 2007-10-18 2013-03-06 キヤノン株式会社 Diagnosis support device, diagnosis support device control method, and program thereof
WO2009059322A1 (en) * 2007-11-02 2009-05-07 President And Fellows Of Harvard College Methods for predicting the development and resolution of acute respiratory distress syndrome
US8414488B2 (en) * 2007-11-13 2013-04-09 Oridion Medical 1987 Ltd. Medical system, apparatus and method
EP2249700B1 (en) * 2008-02-07 2019-04-24 Koninklijke Philips N.V. Apparatus for measuring and predicting patients' respiratory stability
GB2478441B (en) * 2008-09-09 2013-03-27 Somalogic Inc Lung cancer biomarkers and uses thereof
US10359425B2 (en) * 2008-09-09 2019-07-23 Somalogic, Inc. Lung cancer biomarkers and uses thereof
US9003319B2 (en) * 2008-11-26 2015-04-07 General Electric Company Method and apparatus for dynamic multiresolution clinical data display
US8862195B2 (en) * 2010-03-10 2014-10-14 University Of Valladolid Method, system, and apparatus for automatic detection of obstructive sleep apnea from oxygen saturation recordings
FR2959046B1 (en) * 2010-04-19 2012-06-15 Michelin Soc Tech METHOD FOR CONTROLLING THE APPEARANCE OF THE SURFACE OF A TIRE

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
AY Ng, MI Jordan. On discriminative vs. generative classifiers: A comparison of logistic regression and naïve Bayes. Advances in neural information processing systems 14, 2002, pg 841 *
ER Johnson, MA Matthay. Acute Lung Injury: Epidemiology, Pathogenesis, and Treatment. Journal of Aerosol Medicine and Pulmonary Drug Delivery. 2010, Vol 23, No 4. pg 243-252 *
J Ye. Least Squares Discriminant Analysis. Proceedings of the 24th International Conference on Machine Learning. 2007, pg 1087-1094 *

Cited By (105)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11979836B2 (en) 2007-04-03 2024-05-07 Apple Inc. Method and system for operating a multi-function portable electronic device using voice-activation
US11671920B2 (en) 2007-04-03 2023-06-06 Apple Inc. Method and system for operating a multifunction portable electronic device using voice-activation
US11900936B2 (en) 2008-10-02 2024-02-13 Apple Inc. Electronic devices with voice command and contextual data processing capabilities
US11348582B2 (en) 2008-10-02 2022-05-31 Apple Inc. Electronic devices with voice command and contextual data processing capabilities
US12087308B2 (en) 2010-01-18 2024-09-10 Apple Inc. Intelligent automated assistant
US11423886B2 (en) 2010-01-18 2022-08-23 Apple Inc. Task flow identification based on user intent
US11120372B2 (en) 2011-06-03 2021-09-14 Apple Inc. Performing actions associated with task items that represent tasks to perform
US11321116B2 (en) 2012-05-15 2022-05-03 Apple Inc. Systems and methods for integrating third party services with a digital assistant
US10978090B2 (en) 2013-02-07 2021-04-13 Apple Inc. Voice trigger for a digital assistant
US11557310B2 (en) 2013-02-07 2023-01-17 Apple Inc. Voice trigger for a digital assistant
US11862186B2 (en) 2013-02-07 2024-01-02 Apple Inc. Voice trigger for a digital assistant
US11636869B2 (en) 2013-02-07 2023-04-25 Apple Inc. Voice trigger for a digital assistant
US12009007B2 (en) 2013-02-07 2024-06-11 Apple Inc. Voice trigger for a digital assistant
US11388291B2 (en) 2013-03-14 2022-07-12 Apple Inc. System and method for processing voicemail
US11798547B2 (en) 2013-03-15 2023-10-24 Apple Inc. Voice activated device for use with a voice-based digital assistant
US12073147B2 (en) 2013-06-09 2024-08-27 Apple Inc. Device, method, and graphical user interface for enabling conversation persistence across two or more instances of a digital assistant
US11727219B2 (en) 2013-06-09 2023-08-15 Apple Inc. System and method for inferring user intent from speech inputs
US12010262B2 (en) 2013-08-06 2024-06-11 Apple Inc. Auto-activating smart responses based on activities from remote devices
US11257504B2 (en) 2014-05-30 2022-02-22 Apple Inc. Intelligent assistant for home automation
US12067990B2 (en) 2014-05-30 2024-08-20 Apple Inc. Intelligent assistant for home automation
US11810562B2 (en) 2014-05-30 2023-11-07 Apple Inc. Reducing the need for manual start/end-pointing and trigger phrases
US11699448B2 (en) 2014-05-30 2023-07-11 Apple Inc. Intelligent assistant for home automation
US12118999B2 (en) 2014-05-30 2024-10-15 Apple Inc. Reducing the need for manual start/end-pointing and trigger phrases
US11670289B2 (en) 2014-05-30 2023-06-06 Apple Inc. Multi-command single utterance input method
US11133008B2 (en) 2014-05-30 2021-09-28 Apple Inc. Reducing the need for manual start/end-pointing and trigger phrases
US11838579B2 (en) 2014-06-30 2023-12-05 Apple Inc. Intelligent automated assistant for TV user interactions
US11516537B2 (en) 2014-06-30 2022-11-29 Apple Inc. Intelligent automated assistant for TV user interactions
US11087759B2 (en) 2015-03-08 2021-08-10 Apple Inc. Virtual assistant activation
US11842734B2 (en) 2015-03-08 2023-12-12 Apple Inc. Virtual assistant activation
US10998095B2 (en) * 2015-04-08 2021-05-04 Koninklijke Philips N.V. Tool for recommendation of ventilation therapy guided by risk score for acute respirator distress syndrome (ARDS)
US12001933B2 (en) 2015-05-15 2024-06-04 Apple Inc. Virtual assistant in a communication session
US11070949B2 (en) 2015-05-27 2021-07-20 Apple Inc. Systems and methods for proactively identifying and surfacing relevant content on an electronic device with a touch-sensitive display
US11947873B2 (en) 2015-06-29 2024-04-02 Apple Inc. Virtual assistant for media playback
US11853536B2 (en) 2015-09-08 2023-12-26 Apple Inc. Intelligent automated assistant in a media environment
US11809483B2 (en) 2015-09-08 2023-11-07 Apple Inc. Intelligent automated assistant for media search and playback
US10956486B2 (en) 2015-09-08 2021-03-23 Apple Inc. Intelligent automated assistant for media search and playback
US11500672B2 (en) 2015-09-08 2022-11-15 Apple Inc. Distributed personal assistant
US20170285915A1 (en) * 2015-09-08 2017-10-05 Apple Inc. Intelligent automated assistant in a media environment
CN108702539A (en) * 2015-09-08 2018-10-23 苹果公司 Intelligent automation assistant for media research and playback
US11954405B2 (en) 2015-09-08 2024-04-09 Apple Inc. Zero latency digital assistant
US20170068423A1 (en) * 2015-09-08 2017-03-09 Apple Inc. Intelligent automated assistant in a media environment
US11550542B2 (en) 2015-09-08 2023-01-10 Apple Inc. Zero latency digital assistant
US11126400B2 (en) 2015-09-08 2021-09-21 Apple Inc. Zero latency digital assistant
US12051413B2 (en) 2015-09-30 2024-07-30 Apple Inc. Intelligent device identification
US11809886B2 (en) 2015-11-06 2023-11-07 Apple Inc. Intelligent automated assistant in a messaging environment
US11526368B2 (en) 2015-11-06 2022-12-13 Apple Inc. Intelligent automated assistant in a messaging environment
US11886805B2 (en) 2015-11-09 2024-01-30 Apple Inc. Unconventional virtual assistant interactions
US11853647B2 (en) 2015-12-23 2023-12-26 Apple Inc. Proactive assistance based on dialog communication between devices
US11037565B2 (en) 2016-06-10 2021-06-15 Apple Inc. Intelligent digital assistant in a multi-tasking environment
US11657820B2 (en) 2016-06-10 2023-05-23 Apple Inc. Intelligent digital assistant in a multi-tasking environment
US11152002B2 (en) 2016-06-11 2021-10-19 Apple Inc. Application integration with a digital assistant
US11749275B2 (en) 2016-06-11 2023-09-05 Apple Inc. Application integration with a digital assistant
US11809783B2 (en) 2016-06-11 2023-11-07 Apple Inc. Intelligent device arbitration and control
JP2018032394A (en) * 2016-08-25 2018-03-01 株式会社日立製作所 Device control based on hierarchical data
US11150916B2 (en) 2016-09-02 2021-10-19 Intuit Inc. Execution of workflows in distributed systems
US10528367B1 (en) * 2016-09-02 2020-01-07 Intuit Inc. Execution of workflows in distributed systems
WO2018162644A1 (en) * 2017-03-10 2018-09-13 Koninklijke Philips N.V. Patient status monitor with visually strong patient status display
US10825167B2 (en) 2017-04-28 2020-11-03 Siemens Healthcare Gmbh Rapid assessment and outcome analysis for medical patients
US11599331B2 (en) 2017-05-11 2023-03-07 Apple Inc. Maintaining privacy of personal information
US11467802B2 (en) 2017-05-11 2022-10-11 Apple Inc. Maintaining privacy of personal information
US11580990B2 (en) 2017-05-12 2023-02-14 Apple Inc. User-specific acoustic models
US11405466B2 (en) 2017-05-12 2022-08-02 Apple Inc. Synchronization and task delegation of a digital assistant
US11837237B2 (en) 2017-05-12 2023-12-05 Apple Inc. User-specific acoustic models
US11538469B2 (en) 2017-05-12 2022-12-27 Apple Inc. Low-latency intelligent automated assistant
US11380310B2 (en) 2017-05-12 2022-07-05 Apple Inc. Low-latency intelligent automated assistant
US11862151B2 (en) 2017-05-12 2024-01-02 Apple Inc. Low-latency intelligent automated assistant
US12014118B2 (en) 2017-05-15 2024-06-18 Apple Inc. Multi-modal interfaces having selection disambiguation and text modification capability
US12026197B2 (en) 2017-05-16 2024-07-02 Apple Inc. Intelligent automated assistant for media exploration
US11675829B2 (en) 2017-05-16 2023-06-13 Apple Inc. Intelligent automated assistant for media exploration
US11532306B2 (en) 2017-05-16 2022-12-20 Apple Inc. Detecting a trigger of a digital assistant
US11710482B2 (en) 2018-03-26 2023-07-25 Apple Inc. Natural assistant interaction
US11854539B2 (en) 2018-05-07 2023-12-26 Apple Inc. Intelligent automated assistant for delivering content from user experiences
US11169616B2 (en) 2018-05-07 2021-11-09 Apple Inc. Raise to speak
US11900923B2 (en) 2018-05-07 2024-02-13 Apple Inc. Intelligent automated assistant for delivering content from user experiences
US11907436B2 (en) 2018-05-07 2024-02-20 Apple Inc. Raise to speak
US11487364B2 (en) 2018-05-07 2022-11-01 Apple Inc. Raise to speak
US11360577B2 (en) 2018-06-01 2022-06-14 Apple Inc. Attention aware virtual assistant dismissal
US11630525B2 (en) 2018-06-01 2023-04-18 Apple Inc. Attention aware virtual assistant dismissal
US12061752B2 (en) 2018-06-01 2024-08-13 Apple Inc. Attention aware virtual assistant dismissal
US10984798B2 (en) 2018-06-01 2021-04-20 Apple Inc. Voice interaction at a primary device to access call functionality of a companion device
US12067985B2 (en) 2018-06-01 2024-08-20 Apple Inc. Virtual assistant operations in multi-device environments
US12080287B2 (en) 2018-06-01 2024-09-03 Apple Inc. Voice interaction at a primary device to access call functionality of a companion device
US11009970B2 (en) 2018-06-01 2021-05-18 Apple Inc. Attention aware virtual assistant dismissal
US11431642B2 (en) 2018-06-01 2022-08-30 Apple Inc. Variable latency device coordination
US11893992B2 (en) 2018-09-28 2024-02-06 Apple Inc. Multi-modal inputs for voice commands
US11783815B2 (en) 2019-03-18 2023-10-10 Apple Inc. Multimodality in digital assistant systems
US12136419B2 (en) 2019-03-18 2024-11-05 Apple Inc. Multimodality in digital assistant systems
WO2020216437A1 (en) * 2019-04-23 2020-10-29 Espire Technologies Gmbh Device and method for localising or identifying malignancies
CN112930568A (en) * 2019-04-23 2021-06-08 艾斯皮雷科技股份有限公司 Device and method for locating or identifying malignant tumors
US11705130B2 (en) 2019-05-06 2023-07-18 Apple Inc. Spoken notifications
US11675491B2 (en) 2019-05-06 2023-06-13 Apple Inc. User configurable task triggers
US11888791B2 (en) 2019-05-21 2024-01-30 Apple Inc. Providing message response suggestions
US11237797B2 (en) 2019-05-31 2022-02-01 Apple Inc. User activity shortcut suggestions
US11657813B2 (en) 2019-05-31 2023-05-23 Apple Inc. Voice identification in digital assistant systems
US11790914B2 (en) 2019-06-01 2023-10-17 Apple Inc. Methods and user interfaces for voice-based control of electronic devices
US11924254B2 (en) 2020-05-11 2024-03-05 Apple Inc. Digital assistant hardware abstraction
US11914848B2 (en) 2020-05-11 2024-02-27 Apple Inc. Providing relevant data items based on context
US11765209B2 (en) 2020-05-11 2023-09-19 Apple Inc. Digital assistant hardware abstraction
US11755276B2 (en) 2020-05-12 2023-09-12 Apple Inc. Reducing description length based on confidence
US20230207083A1 (en) * 2020-05-27 2023-06-29 Koninklijke Philips N.V. Methods and systems for individualized risk score analysis
US11838734B2 (en) 2020-07-20 2023-12-05 Apple Inc. Multi-device audio adjustment coordination
US11696060B2 (en) 2020-07-21 2023-07-04 Apple Inc. User identification using headphones
US11750962B2 (en) 2020-07-21 2023-09-05 Apple Inc. User identification using headphones
EP4068305A1 (en) * 2021-03-31 2022-10-05 Riatlas S.r.l. Method for displaying on a screen of a computerized apparatus a temporal trend of a state of health of a patient and computerized apparatus
IT202100028643A1 (en) * 2021-11-11 2023-05-11 Riatlas S R L Method of changing a display on a computerized apparatus screen of a health condition of a patient and computerized apparatus

Also Published As

Publication number Publication date
CN104115150B (en) 2018-05-04
BR112014020040A8 (en) 2017-07-11
JP2018014131A (en) 2018-01-25
JP2019169158A (en) 2019-10-03
BR112014020040A2 (en) 2017-06-20
WO2013121374A3 (en) 2014-02-20
RU2014137469A (en) 2016-04-10
JP6541738B2 (en) 2019-07-10
EP2815343A2 (en) 2014-12-24
JP6734430B2 (en) 2020-08-05
JP6215845B2 (en) 2017-10-18
CN104115150A (en) 2014-10-22
WO2013121374A2 (en) 2013-08-22
JP2015513724A (en) 2015-05-14
RU2629799C2 (en) 2017-09-04

Similar Documents

Publication Publication Date Title
JP6734430B2 (en) Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) assessment and monitoring
EP3280319B1 (en) Cardiovascular deterioration warning score
CN101203172B (en) An apparatus to measure the instantaneous patients acuity value
US8473306B2 (en) Method and apparatus for monitoring physiological parameter variability over time for one or more organs
US11751821B2 (en) Systems and methods of advanced warning for clinical deterioration in patients
CN101938939A (en) Apparatus for measuring and predicting patients&#39; respiratory stability
CN103201743A (en) Method of continuous prediction of patient severity of illness, mortality, and length of stay
US20200178903A1 (en) Patient monitoring system and method having severity prediction and visualization for a medical condition
JP2018512237A (en) A tool for recommendation of ventilation treatment guided by risk score for acute respiratory distress syndrome (ARDS)
JP6532460B2 (en) Computerization and visualization of clinical rules and regulations on patient monitoring systems
US11728034B2 (en) Medical examination assistance apparatus
JP2016526450A (en) Apparatus and method for assessing, diagnosing and / or monitoring heart health
CN111317440A (en) Early warning method for patient, monitoring device using the method and readable storage medium
Rehm A Computational System for Detecting the Acute Respiratory Distress Syndrome Using Physiologic Waveform Data from Mechanical Ventilators
Jeong et al. Temporal progress model of metabolic syndrome for clinical decision support system
Chouvarda et al. Respiratory decision support systems
Khalid Data fusion models for detection of vital-sign deterioration in acutely ill patients
CN115836360A (en) Diagnostically-adaptive acute patient monitoring

Legal Events

Date Code Title Description
AS Assignment

Owner name: KONINKLIJKE PHILIPS N.V., NETHERLANDS

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:VAIRAVAN, SRINIVASAN;CHIOFOLO, CAITLYN MARIE;CHBAT, NICOLAS WADIH;AND OTHERS;SIGNING DATES FROM 20131011 TO 20131018;REEL/FRAME:033553/0813

STPP Information on status: patent application and granting procedure in general

Free format text: FINAL REJECTION MAILED

STPP Information on status: patent application and granting procedure in general

Free format text: RESPONSE AFTER FINAL ACTION FORWARDED TO EXAMINER

STPP Information on status: patent application and granting procedure in general

Free format text: ADVISORY ACTION MAILED

STCB Information on status: application discontinuation

Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION